FP7 – 609082 – Collaborative Project
Decision support Advisor for innovative business models and useR engagement for smart
Energy Efficient Districts
DAREED
Deliverable 2.5: Evaluation of existing tool, trade-off and
integration analysis
Authors:
UBRUN, ISOTROL, UNIBO, CLEOPA, CETMA
Reviewers
KIT
Delivery due date:
27.08.2014
Actual submission date
04.09.2014
Status
RE
Deliverable: D 2.5
Organisation: UBRUN
1. Executive Summary
This deliverable entitled “Evaluation of existing tool, trade-off and integration analysis” reports the
results of the work carried out as part of task T2.5 in Work package 2 (System Design and
Knowledge Modelling). The task’s main objective was to analyse relevant information, models
and data from available relevant existing tools and previous projects that are relevant to the
DAREED specific and overall objective. Keeping in line with the aim of the task, this deliverable
first provides a state of the art analyses on available best practices of data collection and decision
making for energy efficiency/savings management based on desk research. It then outlines a
detailed evaluation of existing tool/solution for each of the identified DAREED components in
D2.3 and highlights the related expected context of use, with its benefits and limitations along with
a description of how it could possibly integrate in the DAREED platform. Finally, this report
provides a trade-off and integration analysis of existing energy management tools in order to
allow their exploitation in the analysis and decision support component of the DAREED system.
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CONTENTS
1.
Executive Summary _________________________________________________________ 2
2.
Introduction _______________________________________________________________ 5
3.
State of the Art Analysis _____________________________________________________ 7
3.1
Existing best practices of data collection and decision making for energy efficiency and savings
management _______________________________________________________________________ 7
3.1.1
Existing Best practices of data collection __________________________________________________ 7
3.1.2
Best practices of decision making _______________________________________________________ 12
3.1.2.1
Tariff and Energy Price Determination _______________________________________________ 13
3.1.2.2
Design of Policies and Incentive Schemes ____________________________________________ 14
3.1.2.3
Investment Planning and Management for the Electricity Network ________________________ 15
3.1.2.4
Investment Planning and Management for Green Buildings ______________________________ 15
3.2
4.
Information Systems Evaluation _________________________________________________ 16
Evaluation of existing tools/platforms meeting the DAREED objectives_______________ 18
4.1
Modelling and Simulation ______________________________________________________ 18
4.2
Consumption Monitoring, Analysis and Control ____________________________________ 37
4.3
Energy Management __________________________________________________________ 43
4.4
Decision support and energy awareness __________________________________________ 47
4.4.1
Decision Support Tools and Projects _____________________________________________________ 47
4.4.2
Awareness and Involvement Projects and Tools ____________________________________________ 58
4.5
Existing relevant tools mapped against DAREED components _________________________ 63
5.
Trade-off and integration analysis of existing energy management tools/solutions_____ 65
6.
Conclusions _______________________________________________________________ 67
7.
References _______________________________________________________________ 68
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List of figures
Figure 1: Main practices for data collection ................................................................................................................... 8
List of tables
Table 1: Benchmarking data collection worksheet for 2013 _____________________________________________ 10
Table 2: Evaluation of Existing tools relevant to DAREED Modelling and Simulation Component ______________ 35
Table 3: Existing tools mapped against services provided by Modelling and Simulation _____________________ 36
Table 4: Evaluation of Existing tools relevant to DAREED Consumption Monitoring, Analysis and Control
Component ____________________________________________________________________________________ 42
Table 5: Existing tools mapped against services provided by Consumption Monitoring, Analysis and Control ____ 43
Table 6: Evaluation of Existing tools relevant to DAREED Energy Management Component __________________ 46
Table 7: Existing tools mapped against services provided by Energy Management__________________________ 47
Table 8: Evaluation of Existing tools relevant to DAREED Decision support and Energy Awareness Component __ 56
Table 9: Existing tools mapped against services provided by Decision support _____________________________ 57
Table 10: Evaluation of Existing tools relevant to DAREED Decision support and Energy Awareness Component _ 62
Table 11: Existing tools mapped against services provided by Decision support and Energy Awareness _________ 62
Table 12: Summary of Existing Tools mapped against DAREED Components _______________________________ 63
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2. Introduction
The main goal of this deliverable is the identification and characterization of existing tools that
potentially could be integrated in the DAREED platform. Recalling previous works in the
framework of the project, once the definition of both general and single-component architecture
has been already covered, an in-depth analysis is required to select most promising existing
solutions in the market that meet component requirements. As a result of Deliverables D2.3
“Requirements and design of ICT platform Architecture” and D2.4 “Requirements and design” the
DAREED platform already has been assigned with the set of services to be covered. Moreover,
every single component in which DAREED platform relies has been defined considering aspect
such as input/outputs, data managing or user interaction.
Therefore, this deliverable goes into the following question; “which commercial solutions are
already available in the market capable of meeting service and component needs?” To this
regard, it is important to consider not only the capability to provide the service but also the
operability and potential integration with other solutions. To answer the above question as well as
the integration issue, this deliverable has been structured in three main sections. These sections
aim to cover the logical structure of DAREED, which implies in the following order; data
acquisition, simulation/evaluation, decision making and the cross cutting integration.
The deliverable is structured as follows:
State of the Art Analysis: In this section a first revision is covered to compile the most promising
solutions and best practices regarding data collection, decision making and information system
evaluation.
Evaluation of existing tool/components that meet DAREED objectives: Under this section,
an evaluation is
done of the existing tools available in the market taking into account the
services offered by DAREED that is planned for in the next work packages in the project. So
information will be collected based on:
•
WP3. Modelling and Simulation for ICT platform
•
WP4. Energy Management Tools for ICT platform including the consumption monitoring,
analysis and control
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•
WP5. Decision Support and Energy Awareness
Once again and following the philosophy of this deliverable, this analysis will establish the basis
for the development of next work packages in the project. Additionally, the information collected
will provide potential connection to previous services defined.
Trade-off and integration analysis of existing energy management tools/solutions. In this
last section, a brief trade-off and integration analysis is provided to determine the benefit coming
from the adoption and usage of DAREED system in comparison to the existing solutions. This
section will feed as an input to a more detailed integration analysis that is to be conducted as part
of WP6, Extended Engineering and Integration.
As mentioned above this deliverable will collect basic information for next work packages in the
project; WP3, WP4 & WP5.
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3. State of the Art Analysis
3.1 Existing best practices of data collection and decision making
for energy efficiency and savings management
This section provides an analysis of available practices of data collection and decision making for
energy efficiency/savings management and analyse what could be considered as best cases in
relation to the scope and aims of the project.
3.1.1 Existing Best practices of data collection
The integration of real building and district data into platforms for energy efficiency and savings
management provides to managers, owners and occupants a proper facilities management which
leads to happier users and more comfortable occupants. Adding real time data from building
sensors (for example occupancy, temperature, humidity or energy use) to existing historical
information helps in characterizing buildings and improves modelling and system performance.
For example, occupancy and weather measures can be contrasted to past patterns of heat gain
and heating, ventilation, and air conditioning (HVAC) performance to reduce energy demand
while keeping comfort parameters [46].
For specific building and energy data collection, three categories can be defined in regard to the
practices that are usually employed: simple level, intermediate level and advanced level, which
are connected to the way data is retrieved, manually, through automatized services and devices
respectively [43].
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Figure 1: Main practices for data collection
At the simple level, building manager tracks and records monthly energy usage, cost data and
other basic information of the building and its use. It is important to identify the building and
categorize it in a group, in order to enable comparisons between buildings with similar
characteristics and to establish trends and statistics. Buildings could be arbitrarily divided in
categories such as commercial, residential or industrial. However, this classification only suffices
a very narrow scope. Thus, there is a need to expand the building types to include more diverse
uses and have specific information about sub-types of buildings [43] [46].
When carrying out data collection, few government surveys have embraced the use of interactive
online tools as a means to build awareness about data collection and to encourage survey
participation. Innovative strategies involving interactive online tools may be able to engage users
who are interested in learning about their energy consumption.
An example of this practice could be found in the increasing interest in using data for
benchmarking building performance at community level, as seen in places like New York City,
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with the help of the ENERGY STAR Portfolio Manager1, developed by the U.S. Environmental
Protection Agency [48] [49].
They have achieved a relatively simple approach, in a form similar to an online community, where
members are offered online calculators and other tools that would allow them to measure and
track energy and water consumption, as well as greenhouse gas emissions, benchmarking
building results to compare their energy use and features to those of an average building type or
members in their community. To get started, users are asked to record energy bills and some
basic information about buildings.
The benchmarking strategy would not only engage users but would also produce data that could
be further analysed by public administrations and governments. For example, the U.S. Energy
Information Administration (EIA) employs energy data to track trends in users’ behaviour.
Concerning benchmarking activities and model calibration support, it is advisable to create
standardized data collection templates which will involve tasks as identifying key parameters and
operation benchmarks, defining a standardized data collection template for different building
types, establish a graduated approach for data collection with necessary and convenient
parameters based on sensitivity analysis, and developing strategies to collect data which has high
impact on benchmarking but are difficult or expensive to collect [50].
Table 1 shows a couple of template forms of Property Use Attributes use for benchmark buildings
over 50,000 gross square feet in the District of Columbia (US) as it is required by legislation [49].
Hotel
Required
_______ Gross floor area (sq. ft.)
_______ # of rooms
_______ # of workers on main shift
_______ # of commercial refrigeration/freezer units
1
http://www.energystar.gov/buildings/facility-owners-and-managers/existing-buildings/use-portfolio-manager?s=mega
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_______ On-site cooking – yes or no
_______ Percent of floor area that is cooled (in 10% increments)
_______ Percent of floor area that is heated (in 10% increments)
Optional
_______ Hours per day the guests are on-site
_______ Number of guest meals served
_______ Floor area of full-service spas
_______ Floor area of gym/fitness center
Laundry processed at site (choose one: no laundry facility, linens only (e.g.
_______ bed/table
_______ linens), terry only (e.g. towels, bathrobes), both linens and terry)
_______ Annual quantity of laundry processed on-site
_______ Average Occupancy (%)
Supermarket/grocery store or Wholesale club/supercenter
Required
_______
Gross floor area (sq. ft.)
_______
Weekly operating hours
_______
Workers on main shift
_______
On-site cooking – yes or no
_______
# of walk-in refrigeration/freezer units
_______
Percent of floor area that is cooled (in 10% increments)
_______
Percent of floor area that is heated (in 10% increments)
_______
# of open or closed refrigeration/freezer cases
_______
# of registers and/or personal computers
Table 1: Benchmarking data collection worksheet for 2013
At the intermediate level, data is more regularly collected and the information is richer in detail.
Thanks to more detailed information it is possible to analyse the whole building’s energy usage.
Energy management decisions could be made proactively by means of more accurate, complete
and consistent data and analysis. Lower operating costs, longer equipment life and the
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improvement of buildings’ occupant comfort are also benefits of having deeper knowledge of
building’s situation [42].
Intermediate level of data could embrace weekly utility bill information, building maintenance
information, data from human resources department and meter data. Monitoring frameworks
based on smart meters and BMS are used to manage this kind of information to complete several
analyses and identify what equipment runs under its optimum operation, which types of projects
are worth to invest in, and even what level of investment is the most adequate [54].
At this level, more advance tools are needed to store, track and analyse gathered data in order to
identify opportunities for better energy management, knowing where and when energy is
consume (see [53]). Besides, with precise tools, data could be store and display in various
formats as dashboards and reports to help user to consider the information and to facilitate
decision-making.
At the highest level of information, the advance level, devices, data collection and analytical tools
are employed in conjunction in order to achieve better results. Advance automation technologies
and systems integration are used to measure, monitor, control and optimize building operations
and maintenance.
While studying and operating building’s systems separately might be
challenging for users (lighting, HVAC, fire and security, distributed generation), having all systems
integrated in one facilitates understanding energy performance and make clearer which decisions
are to be made to optimize energy use in real time.
Through centralized network integrated BMSs, building data can be translated into higher-level
info, to track building performance comparing it to a baseline, define automatic systems controls
and even respond to real-time changes in energy demand in the smart grid [51] [52].
For these actions, it is important to have a precise model of the case study which requires a
significant amount of data (building data, systems parameters, consumption data …), to be
calibrated. Once, running and calibrated, models are a very powerful tool capable of analyse and
simulate actual situation and consider complex ‘what-if’ scenarios. Models [45] [47] can also be
used to evaluate the cost/benefit of proposed measures and actions and assist with life-cycle cost
analysis.
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Advance systems working with cloud computing can provide scale, storage and processing power
to manage the data generated by building sensors, meters and controls. It provides a platform to
connect disparate data sources to generate actionable information and optimize building
performance. By accessing new cloud-based data sets, users can combine public information like
weather forecasts and energy pricing with private building information and energy usage to
enable new insights into building energy management. Cloud computing enables building
management solutions to deliver storage, data access, analytics, and application services to
support cost-effective massive data aggregation. Recent researches [42] [44] point towards the
development of potential applications in internet networks, the design of better algorithms, and
the optimization of smarter networks.
Finally, we would like to mention here several factors that are to be considered when empirical
data are collected:
•
A list of requirements must be defined driven and based on use cases and metrics.
•
Building owners and service providers will probably be the first interested in acquiring data
and ready to share it, however it would be wise to have standard templated for any legal
agreements to address data confidentiality.
•
Collected data should be map into a common data formta to enable analysis, in which
each data field should be clearly defined. Alarms and notification could be sent to inform
of data type errors, out of range data, missing values, ect.
•
At publishing data, confidentiality must be considered and anonymizing should be apply
as need
3.1.2 Best practices of decision making
Decision Support is a broad topic, encompassing a number of diverse activities. The business
analytics field provides an effective classification scheme for such activities, grouping them into
descriptive, predictive, and prescriptive analytics.
In detail, descriptive analytics is concerned with offering to the decision maker a clear and
understandable view of the available information. A number of analysis techniques can be used to
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make sense of large quantities of data, while specific technology may be needed for its
manipulation.
The goal of predictive analytics is to allow the decision maker to evaluate the effects of possible
scenarios (e.g. possible choices or possible future events). Such evaluation is accomplished by
designing simple analytical models or complex simulators. Predictive analytics allows a decision
maker to compare a set of decisions by so-called what-if analysis, i.e. by designing scenarios for
each set of decisions an observing their consequences via simulation.
Finally, prescriptive analytics goes one step further by offering to the decision maker
recommendations about the best courses or action. For relatively simple problems, this can be
done by constructing, evaluating and ranking a set of scenarios, i.e. by performing what-if
analysis in an automated fashion. In many cases, however, the number of scenarios to be
evaluated may grow very vast, making this approach extremely time consuming (if all scenarios
are considered) or poorly effective (if only a small subset is evaluated). In this kind of situation,
more powerful optimization techniques need to be used for finding the set of decisions to be
recommended to the user.
The DAREED project covers to some degree all the three classes: monitoring and analysis tools
will be developed in WP4, while predictive models and a simulator will be designed in the context
of WP3. Finally, most tasks from WP5 aim at devising and building recommendation systems.
This brief section will however focus mostly on prescriptive analytics, the most advanced and the
most under-represented class of decision support methods. Only a few descriptive and predictive
approaches will be mentioned. Moreover, the overview provided in this section will be limited to
approaches that have been published in research papers (and perhaps tested on prototypes), but
not included in actual tools: decision support tools (both existing and under development) will be
extensively covered in the remainder of this document. The presented approaches are grouped
by the kind of decisions they are meant to support.
3.1.2.1 Tariff and Energy Price Determination
With the advent of distributed generation, renewable energy technologies, and smart meters, the
complexity of determining optimal energy prices and tariffs has become considerably higher.
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From the point of view of an energy distributor, the electricity price determination requires to take
into account consumption and production variability due to renewable energy systems [1][2]. In
DAREED, however, we are mainly interested in the perspectives of market-side energy providers
(who care about new business models to reduce consumption and costs) and customers (who
care about energy savings). In this spirit, works [3] [4] [5] analyze opportunities to exploit dynamic
pricing and demand response (i.e. price dependent consumption variability) to reduce
consumption peaks, electricity costs, and to allow energy savings.
In this context, it is widely acknowledged that consumers often fail to exploit dynamic pricing to its
full potential (i.e. they do not behave as rational agents), either because of comfort issues
associated to shifting loads (e.g. running household appliances during off-peak time) or simply
because of lack of information or distorted price perception (see [6]). This phenomenon greatly
increases the complexity of designing good simulators for demand response and dynamic pricing.
Most approaches deal with the issue by adding constraints (e.g. limits to load shifting) to rational
agents (see for example [7]). The same approach is used in the predictive models employed
within optimization approaches to obtain prescriptive analytics systems. One such example is
work [8], which takes the perspective of an energy provider and tackles the problem of
determining day ahead dynamic prices.
3.1.2.2 Design of Policies and Incentive Schemes
A number of scientific approaches have tackled the problem of supporting policy making activities
to promote the adoption of renewable energy generation and energy efficient technologies. In this
scenario, the main user is always a policy maker (such as a city level of regional level authority),
while the targets for the policy may be either energy distributors (owners of part of the electricity
grid) or common citizens.
Many approaches in this field make the assumption that the policy maker is capable of directly
implementing energy efficiency improvement measures (e.g. installing new generators,
performing building improvements...). This is the implicit assumption behind many predictive
approaches that aim at assessing the energy consumption of whole cities or urban districts, such
as [9] [10] [11] [12]. This kind of approach enables the identification of specific goals for local
policies via what-if analysis. The process can be made automatic if a predictive model is coupled
with optimization technology (e.g. [13] [14]).
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As main drawback, this class of approaches disregard the facts that policy makers usually lack
the ability to take direct actions and must try to affect the behaviour of citizens and energy
distributors by designing incentive schemes. Supporting this activity is a much more complex
task, which involves a thorough analysis of specific incentive schemes, the design of predictive
models that take into account behavioural aspects (see [15] and [16]), and the combination of
such models with optimization technology. In [17] bi-level optimization is combined with a
simplified model to design incentive schemes targeting energy distributors. Several interesting
approaches targeting common citizen at regional scale have been developed in the context of the
e-Policy project [18], coordinated by the same research group of the University of Bologna that is
a partner in DAREED.
3.1.2.3 Investment Planning and Management for the Electricity Network
Energy Distributors (meaning owners of part of the electricity networks) are not among the main
users of the DAREED platform, which instead is meant for market-side member of energy
provided organizations. Despite this, it is worth to mention a few decision support approaches for
the management of the local electricity network and for planning long-term investments, because
the scale of the problems they tackle is similar to that of DAREED.
Many prescriptive approaches in this field have an emphasis on distributed generation systems
and focus on planning number, type, and local of generators (based on renewable energy or not)
and other electrical equipment on the network. Works [19] [20] [21] fall into this class. Other
approaches (e.g. [22] [23] [24]) deal with the run time management of the existing infrastructure
and in particular renewable energy technology. A last group of approaches use optimization
methods to tackle the problem of designing district energy systems, where heating, hot water, and
electricity distribution are deployed and managed in an integrated fashion to achieve a higher
efficiency [25] [26].
3.1.2.4 Investment Planning and Management for Green Buildings
Decision support approaches at a building scale are considerably more widespread and mature
than those at district scale, thanks in no small part to the availability of powerful and accurate
building-level energy simulators. Unfortunately, the same kind of simulator is unlikely to scale well
to whole urban district, which limits the reusability of this class of approaches in DAREED.
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Despite this, it is worth to mention some building-level decision support approaches, because
they deal with efficiency improvement techniques (e.g. retrofitting technology) that will be taken
into account in DAREED, although on a larger scale system.
Decision support approaches at building level can be classified in two groups: those that try to
make a better use of existing equipment within a building and those that focus on one-time
actions to improve the energy efficiency (e.g. adding insulation) or with design time decisions
(e.g. deciding the orientation).
In the first group, work [27] tries to assess the saving potential of behaviour changes. Among the
prescriptive approaches, [28] employs a rule-based expert system to let a user monitor and
optimize the daily management of a building. Automatic load control approaches (there are many
of those) can also be classified in this group.
Many approaches in the second group employ multi-objective optimization methods (such as
Genetic Algorithms) and simulation to obtain a number of non-dominated solutions corresponding
either to appealing efficiency improvement actions or promising design decisions. Works in this
group include [29] [30] [31] [32] [33] [34].
3.2 Information Systems Evaluation
In any consideration of adopting new technology, attention must be paid to the benefits (i.e.
strengths) and costs or the limitations of the technologies to be implemented [35].The emergence
of modern information systems and the rise of mobile technologies have opened up both new
perspectives and challenges for the organisations. Nevertheless, cutting edge digital
communication comes filled with both potential opportunities and risks. Therefore, the implications
of these new digital frontiers and opportunities from the perspective of are now also on the
governmental agenda [36].
Information Systems (IS) managers are increasingly aware of the possibilities of software
solutions to improve the performance of organisations and provide potential benefits to their
stakeholders and business partners. However, IS managers in private and public sector
organisations have found it increasingly difficult to justify an expansion in Information and
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Communications Technology (ICT) spending [37]. They are under increasing pressure to find a
way to measure the contribution of their organizations’ ICT investments to enhance performance,
as well as to find reliable ways to ensure that the value from these investments are actually
realized [38]. This can be mainly due to a lack of understanding of the impact of ICT investment in
most of the organizations [39]. Therefore, it is important for managers to understand better the
impact of IS on organisational performance, in particular understanding the benefits and costs or
the limitations related with the financial and social capital investments in developing such
infrastructures [40]. Failure of such understanding can lead to disastrous consequences such as
inappropriate resource allocation [41]. However, if managers’ can better understand this, it can
then help an organisation to better utilise its resources and improve its overall efficiency. While it
is important to assess and recognise the benefits of an IT system, in order to complete a robust
IS evaluation, it is equally important to understand the cost implications of an IS project [40]. As
organizational spending on IT adoption is both a necessity and fairly large proportion of turnover,
cost analysis is used as a measure to assess the effectiveness of an organizations IT
expenditure.
Like any other IT investment, decision support systems such as DAREED in private or public
organisations also need to be planned as they require organisational change to culture, people,
structure and processes to be managed in order to obtain effective results. Therefore, an
evaluation of existing software solutions that might be potentially relevant to the services or
functionalities provided by DAREED platform is useful to gain a better understanding of the
existing software’s functionalities that might help provide better understanding for the DAREED
project.
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4. Evaluation of existing tools/platforms meeting the DAREED objectives
This section provides an evaluation of the existing tools or software platforms (e.g. EnergyPlus Energy Simulation Software etc.) available
in the commercial market and other related EU projects relevant to the DAREED objectives/services. For each DAREED services (refer to
D2.3 and D2.4 for list of service names), existing tools that could be adopted have been evaluated by addressing the benefits and
costs/limitations of their integration in DAREED platform.
4.1 Modelling and Simulation
Simulation tools can represent the energy consumption of systems related to buildings in a wide range, typically in annual periods. In
general, analysis tools are useful in predicting the energy consumption, in the process of design and verification. Simulation tools use
different physical models for the representation of the building's energy, so they can be used in all stages of the life cycle of the building and
for every instant of time considered. Although it is possible to consider any instant of time, an energy simulation often is realized considering
a hourly time-step; the reason is to be found, primarily, in the format of the input data (such as climate data, provided through hourly
statistical averages) and secondly in the fact that a time step less than one hour would generate too much long computation time, beyond
the scope of the simulation.
At the time, the tools of dynamic energy simulation of buildings are mainly based on one-dimensional heat transfer of building elements
through heat zones. This assumption dramatically simplifies the geometric data and allows a high computational speed. The geometry of a
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building is the basis of the initial data for an energy simulation. Furthermore, sometimes, also external objects have a significant impact on
the energy model, drastically reducing external solar loads transmitted within the building. The external loads depend exclusively on the
climate data used in the simulation. There are thousands of sets of weather data for many cities around the world [55]. Obviously it's
important to use data that do not refer to a specific year, but rather the statistical weather data, which refer to a specific place.
The main objective of the energy simulation software is to compare different strategies to optimize energy consumption and maintenance
costs. The expected value of the energy consumption in a simulation, with certain assumptions, it’s rarely accurate. In order to validate
simulation tools, the International Energy Agency (IEA) has developed numerous validation tests [56] [57].
In this section, a brief description will be made of some software based on dynamic models. A complete list of the tools available to date is
published in the "Building Energy Software Tools Directory" (U.S. DOE, 2007) [58]. In particular, the discussion will focus, at first, on the
calculation codes DOE-2 and EnergyPlus, with the main graphical interfaces RIUSKA and eQUEST for DOE-2, and DesignBuilder and
Openstudio for EnergyPlus. Afterwards, other software will be taken into account, like ESP-r, TRNSYS, TAS, MLE+ and JEPlus, describing
main characteristics, potential and limitations.
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Existing Tool
DOE-2
Benefits
Costs/Limitations
DOE-2 energy model normally takes less DOE-2
does
than a minute, or few minutes in case of simultaneously
Service Contribution
not
solve Due to the fact it refers only to
the
building a single building, it might serve
large buildings, to complete an annual envelope thermal dynamics with only for evaluation, simulation
simulation; its four simulation sub-modules the HVAC system operating and
are
executed
SYSTEMS,
sequentially:
PLANT
and
LOADS, performance;
ECONOMICS; relationship
calculation engine is designed to analyze the different
there
is
between
modules
(as
forecast
for
a
single
a building.
the
for
energy performance of whole building during example between LOADS and
all stages of life; it predicts the hourly energy SYSTEMS modules) and this
use and energy cost of a building given may affect the results of the
hourly
weather
information,
a
building simulation; another limitation is
geometric and HVAC description, and utility the difficulty in appropriately
rate structure
modeling
the
different
plant
elements.
EnergyPlus
EnergyPlus is built upon the best features of Input data can be entered only Due to the fact it refers only to
DOE-2.2 and BLAST; it is open- source; it’s through text files, however there a single building, it might serve
based
on
the
resolution
of
the are
many
graphical
user only for evaluation, simulation
thermodynamic equations, thus producing interfaces; the biggest limitation and
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for
a
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better results than the DOE-2; it produces is
the
lack
of
a
graphical building.
the modeling of air flows between thermal interface capable of providing all
zones and a more realistic definition of the functionality of the software.
control of the HVAC and cooling systems Besides
DesignBuilder,
there
and radiant heating; self-sizing of many are several other very versatile
specific parameters for each component, interfaces,
making the results more accurate and Openstudio
as
for
[60].
example
Although
reliable than the DOE-2; it allows two main EnergyPlus provides a series of
types of simulation: energy analysis and links
to
other
simulators
ASHRAE method of calculation of thermal (COMIS, SPARK), there are still
loads [59]; the user can select and schedule limits to their use. For example,
any variables available for output, specify the a
parallel
simulation
of
a
time step or environmental intervals; it detailed analysis of the air flow
accepts input data from different sources (COMIS)
with
an
energy
(CAD programs); modules, such as COMIS, simulation is reliable only for
SPARK, TRNSYS can be incorporated into a not-pressurized HVAC systems
simulation
to
combine several
physical [61]
phenomena; it supports 3-D geometry input
RIUSKA
The calculation engine is based on DOE-2 RIUSKA provides simulations The tool cannot be applied to
code; it supports a variety of
design using the model SYSTEMS, DAREED platform, because of
21
Deliverable: D 2.5
Organisation: UBRUN
alternatives through so-called "cases": the limited to a few air conditioning its limitations
user can create different alternatives based systems.
on a specific case and evaluate the effects of RIUSKA does not use PLANTS
different configurations of the model; there and ECONOMICS modules, so
are four different systems for air conditioning it
does
not
provide
any
systems: a constant flow, variable flow, the simulation on water systems for
air systems. This limitation may
so-called “cold beams” and induction units
affect the applicability of the
software mainly for certain types
of buildings and facilities. In
addition, RIUSKA inherits all the
limitations of DOE-2 calculation
engine
eQUEST
eQUEST has been developed primarily to A limit is the lack of reliable Its most useful feature for
analyze the energy performance during all import
geometry
from
CAD DAREED could be the detailed
phases of a building's design; it’s a free software
simulation of the building and
energy simulation tool that allows you to
the estimate of how much
have all the functions of DOE-2.2 simulation
energy it would use
engine; it allows a valid comparison of
different design options based on user22
Deliverable: D 2.5
Organisation: UBRUN
specified parameters; the software allows
both a rapid display of the results and an indepth analysis to assess the effect of
changes
in
consumption
parameters
and
of
occupant
energy
comfort;
eQUEST has multiple types of HVAC
(Heating, Ventilation and Air Conditioning); it
allows high insertion speed of data through
the use of a wizard
DesignBuilder
It’s
the
most
complete
interface
to The tool does not yet support It defines a suitable model that
EnergyPlus available today; is primarily the full potential of EnergyPlus; could be applied to DAREED.
developed as a tool to be used to facilitate all in the structure of the software Due to the fact it refers only to
phases of the design process; it consists of a there are HVAC systems that a single building, it might serve
simplified CAD, wizards and more compact provide simple and compact only for evaluation, simulation
configurations for the modeling of air flows; it definitions, but do not include and
allows the possibility of different options for detailed
information
on
the building.
the wrapper facade, through the analysis of components and their topology.
solar location in the site, fluid dynamics Another limitation is the inability
simulation and sizing of equipment and to import the input file from
23
forecast
for
a
single
Deliverable: D 2.5
Organisation: UBRUN
HVAC systems [62]; it is simple and easy to EnergyPlus, forcing the user to
use; it allows you to set various parameters create a 3D model geometry for
for the simulations: activities for the entire the energy analysis [61]
building can be set and, among other things,
the use, indicating the number of persons
per square meter and assigning the value of
the metabolic rate; the setpoint for heating
and cooling can be set, and the value of
setback, that is the value of minimum /
maximum temperature which controls the
switching of the system; setpoint ventilation,
the
minimum
air
renewal,
the
target
luminance in the area, the number of
computers, the construction characteristics,
the size of the openings and shielding
systems, the lighting and even the HVAC
system can be set; also the output of
simulation (heating, cooling, or in dynamic
mode) can be defined; you can switch to
EnergyPlus exporting IDF file of the project
24
Deliverable: D 2.5
Organisation: UBRUN
and saving the simulation performed
Openstudio
This tool is a graphical interface to support A limit may be given by the It defines a suitable model that
whole
building
energy
using great
modeling
flexibility
of
three- could be applied to DAREED.
EnergyPlus and Radiance [63] for the dimensional model which can Due to the fact it refers only to
analysis of lighting. A version of the software lead
the
user
to
model a single building, it might serve
is contained in a plug-in for SketchUp [64]; it architectural objects negligible only for evaluation, simulation
includes
modules
for
display
and for the purposes of simulation and
modification of components constituting the efficiency,
casing, an interface for modeling the internal and
especially
loads and air conditioning systems for air modeling
and water. The Radiance module can be
used as an instrument of integration to
energy
simulations.
The
module
"ParametricAnalysisTool" provides a range
of energy alternatives to the specific case of
the
analysis,
while
the
module
"RunManager" allows you to run energy
simulations
in
parallel
EnergyPlus
engine.
The
through
the
results
are
displayed through the form "ResultsViewer"
25
increasing
forecast
uptime building.
computational
for
a
single
Deliverable: D 2.5
Organisation: UBRUN
providing an overview of the analysis,
simplified or detailed; there are few limits to
the use of this software.
ESP-r
ESP-r is capable of emulating any physical The only limit that can be It defines a suitable model that
model regarding the energy performance of attributed to it is the difficulty in could be applied to DAREED.
buildings.
The
calculation
code
is use, especially for those not Due to the fact it refers only to
deterministic, based on the solution of the familiar with the thermo-physical a single building, it might serve
equations of thermodynamics, starting from building
only for evaluation, simulation
data input (climate data, thermo-physical
and
characteristics of the housing, free internal
building.
contributions, etc.). For the solution of the
balance equations of mass and energy of the
structure is used the finite volume method
[65]. The software, being versatile and
flexible to the needs of the user, allows a
rigorous analysis of the energy performance
of the building and a detailed monitoring of
the environmental system; the software
consists of three main modules: Project
Manager,
Simulator
and
the
Results
26
forecast
for
a
single
Deliverable: D 2.5
Organisation: UBRUN
Analyzer [66]; there are also other modules,
such as an accurate general database
(climatic profiles, wind profiles, components,
properties of windows, etc..) and modules for
the definition of shading systems, factors of
view, exchange coefficients for convention,
etc.. The modular structure allows to perform
thermal, acoustics, lighting analysis, etc.
simultaneously
TRNSYS
TRNSYS is an energy simulation software The resolution technique of heat It defines a suitable model that
made up of several modules. The database exchanges is the obsolete "z- could be applied to DAREED.
includes many of the components for thermal transfer
function",
which Due to the fact it refers only to
and electrical energy use and modules to complicates the modeling of a single building, it might serve
manage the input data. Each component can building envelopes with high only for evaluation, simulation
be connected to the simulator. The modular thermal
inertia
structure of the program provides a great simplification
for
and
a and
modeling building.
flexibility by facilitating the use of any through the "star temperatures"
components not included in the default of the thermal zones internal
library; TRNSYS has a robust solver of surfaces
27
forecast
for
a
single
Deliverable: D 2.5
Organisation: UBRUN
algebraic differential equations that can read
and process an input text file; TRNSYS is a
tool very “transparent”: users can assess the
value of any variable of the system during
the phases of the simulation (for example,
any temperature, flow, heat transfer, etc.);
users
can
develop
their
own
models,
extending the functionality of the program to
meet their needs; the component that
exemplifies the building includes all the
parameters
exchange
to
represent
between
the
surfaces,
radiative
convective
exchanges, solar radiation, etc.. The GUI
facilitates the inputs and outputs of all the
components that make up the building
envelope and systems, while the recent
plug-in for SketchUp allows to model the
building’s geometry in an easy and fast way;
the tool has good technical documentation
[67,68,69], the availability of computer code
28
Deliverable: D 2.5
Organisation: UBRUN
and finally the ability to connect to other
programs such as MATLAB [70], Excel [71],
EnergyPlus and COMIS
TAS
TAS (Thermal Analysis Software [72]) is a TAS is not intended for detailed TAS
follows
DAREED
program for thermal simulation in dynamic services layout design
objectives and could be used
regime of buildings and installations. TAS is
as reference, although it refers
based on a multi-core technology that allows
only to a single building.
using, in parallel, all the processor cores. In
fact, TAS is able to perform more than one
dynamic simulation in parallel, making really
fast the operations. The program is very
powerful in tests for ventilation, implementing
different
testing
methods
for
natural
ventilation, mechanical and hybrid, and to
control overheating; for these purposes, a
module has been developed for the analysis
of
two-dimensional
computational
fluid
dynamics, which allows you to quickly and
intuitively verify the effectiveness of the
choices of natural or mechanical ventilation,
29
Deliverable: D 2.5
Organisation: UBRUN
adopted by integrating the ability to insert
thermal loads within the environment and to
link them to appropriate controls, adjustable
over time. The program consists of a suite
that includes a three dimensional modeler
(3D Modeller), which enables to model the
building directly into the software or import
and export to gbXML models [73], a dynamic
simulator (Simulator Building) which is set
with the parameters of the simulation and
runs the calculations, and a display of results
(result Viewer) with which it is possible to
filter the results and save and export data in
various formats; TAS has several tools for
the export of the main results of calculation
(thermal
loads
for
heating,
cooling,
humidification and dehumidification, power
required for heating and cooling, a single
room or the building interior), verification of
comfort (calculation of the Predicted Mean
30
Deliverable: D 2.5
Organisation: UBRUN
Vote (PMV) and of the percentage of
dissatisfied (PPD), for any room or the entire
building) and verification of the frequency of
each
type
of
physical
phenomenon
calculated in the building; the suite also
includes a module for dynamic simulation of
systems based on components of the
system, which allows to model various types
of plumbing and air system, powered with
conventional or renewable energy sources.
There are modules for managing archives of
climate
data,
calendars,
the
materials
internal
and
conditions,
construction
components
MLE+
MLE+ is a MATLAB toolbox for co-simulation Missing features such as GUI MLE+
with EnergyPlus: it comes in the form of for
native
MATLAB
objects
that
hide
viewing
the simulation
and
results,
follows
DAREED
analysing objectives and could be used
design as reference, although it refers
researcher most of the complex operations optimization, controller synthesis only to a single building.
necessary for reading and sending data in and testing.
EnergyPlus. MLE+ can be used as a utility in
31
Deliverable: D 2.5
Organisation: UBRUN
Matlab
or
Simulink.
MLE+
provides
a
graphical front-end to specify the inputoutput variables to be exchanged between
EnergyPlus and Matlab for the co-simulation.
After loading the idf file that describes the
building, in the control section you can
configure the variables; the MLE+ front-end
allows viewing, in a friendly manner, all
available input and output variable; it is
possible to automatically create a file with
control policies to be integrated with the
code that implements the control that we
want to perform on the same variables, in
terms of range and constraints, or you can
also load a Matlab file already implemented.
The MLE+ tool also provides the ability to
interface with devices that use the BACnet
protocol, in fact it allows the identification of
BACnet devices, read/write operations and
controller configuration for BACnet devices.
32
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Organisation: UBRUN
The core of MLE+ provides an API that
contains a set of MATLAB functions and
classes of low-level that are responsible for
all other components of MLE+.
The MLE+ utilities are developed using the
API
to
facilitate
simulation,
the
development
monitoring,
analysis
of
and
optimization of building energy.
Some MLE+ utilities are:
•
A
function
extraction
of
for
the
parameters
analysis
and
from
the
EnergyPlus file description of the building
•
An editor that allows you to automate
the configuration and mapping of external
variables for EnergyPlus
•
A visualizer of simulation results that
can load, plot and export the simulation
results from EnergyPlus to Matlab
•
An image visualizer that exports the
33
Deliverable: D 2.5
Organisation: UBRUN
building geometry from EnergyPlus allowing
the visualization in Matlab.
In addition, MLE + provides a library of
Simulink blocks for co-simulation between
Simulink and other simulators. The Simulink
blocks are essentially a wrapper of the MLE+
core for Simulink. This facilitates the design
based on the model for the building energy
control.
JEPlus
JEPlus provides a GUI designed for the JEPlus
creation
of
parametric
simulations
allows
parametric JEPlus
follows
DAREED
with analysis, but not optimization objectives and could be used
EnergyPlus, allowing the collection of the analysis.
as reference, although it refers
results in CSV tables; the input consists of
only to a single building.
the *.idf file, inside which parameters can be
defined, that vary with a certain step within
the range determined in the course of
simulations; the projects and the results of
parametric simulations can be saved in XML
format, exportable on different platforms; the
parameters are organized in a tree structure
34
Deliverable: D 2.5
Organisation: UBRUN
that reflects the dependencies between
parameters and can be imported and
exported through the use of CSV files.
The IDF input can be integrated through the
use of IMF files, used to define the
parameters to be found in the IDF file by EPmacro called in the .IMF file. This makes it
possible to define different descriptions of
materials, HVAC systems, or different sets of
values of the control variables, and use them
as parameters to be recalled in the input file;
JEPlus allows selecting a building model (the
IDF or a set of files IMF), putting search
strings in place of parameters and specifying
all possible values for parameters. JEPlus,
then, choosing a set of values to be inserted
appropriately in the model building, evokes
EnergyPlus
Table 2: Evaluation of Existing tools relevant to DAREED Modelling and Simulation Component
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Deliverable: D 2.5
Organisation: UBRUN
The following table relates the presented tools with the services associated to Modelling and Simulation Tool in DAREED platform. None of
the studied tool allows you to analyse energy efficiency at district level, but only at building level; this fact is a really important element of
originality of DAREED solution.
Tool name
S01
S02
S03
DOE-2
X
X
EnergyPlus
X
X
RIUSKA
X
X
eQUEST
X
X
DesignBuilder
X
X
Openstudio
X
X
ESP-r
X
X
TRNSYS
X
X
TAS
X
X
MLE+
X
X
JEPlus
X
X
S04
S05
S06
X
X
X
X
X
X
Table 3: Existing tools mapped against services provided by Modelling and Simulation
36
S07
Deliverable: D 2.5
Organisation: UBRUN
4.2 Consumption Monitoring, Analysis and Control
Regarding consumption monitoring and demand characterization, there are several commercial tools that could be found in the market.
Some of them have been designed to cover particular requirements, as those targeting domestic consumers, while others count with many
functionalities and could work in a variety of environments and for different type of users. In the table below, a set of tools existing in the
market have been described following a cost-benefit relation. Some are closer to DAREED concept that others though all address
monitoring and analysis towards the same direction. In some cases, an existing tool could serve to perform the desired services, however,
as the platform includes many other services covered by other components it might be extremely difficult to combine them working together
and the initial benefits are reduced by integration costs.
Existing Tool
Benefits
Costs/Limitations
The Olw is a company that has The
electricity
developed
monitors
series of smart meters and energy cases (domestic, small businesses, domestic consumptions of a single
commercialized
application
a distinguishing
three
is
designed Although unlikely, due to its design
The OLW’s Smart
and
web
Service Contribution
particular
use limitations, it might serve to monitor
monitors for the domestic and small and distributed generation systems).
business
requirements.
consumption point that could be
Information The higher disadvantage is hardware supported
retrieved through monitoring can be compatibility
consulted via web dashboards which designed
to
as
the
work
software
with
its
is services.
37
other
Then,
to
own functionalities
display energy consumption in a user- products. It cannot manage more than shall
friendly way. One of its strengths is that one building.
by
be
example
both
used
in
in
order
DAREED
offer
full
applications
parallel,
to
for
received
Deliverable: D 2.5
Organisation: UBRUN
data can be aggregated by different
statistics or notifications, as The
time periods. Besides, it enables data
Olw’s application works separately.
exportation.
Hardware installation is minimal.
The combination of products enables to Targeted
Smart clamp,
measure consumption and generation business applications.
smart appliance
of a house or small business, measure Specific application for the hardware. It will require the use of separate
and widgitboard
and
connect/disconnect
to
domestic
and
small As the previously introduced tool,
Powertracker’s
any use in DAREED’s smart grid
specific is not possible to integrate it in services to cover functionalities as
devices. The company offers an online DAREED web.
a set of consumption-generation
service to interact with the hardware
points that could not be satisfied by
and observe live and average energy
Powertracker’s solutions.
measures.
Hardware installation is minimal.
It
could
serve
to
monitor
single
distributed generation systems.
tool
which
increases It
shares
many
functionalities
Lucid’s
Very similar to DAREED, this tool Commercial
BuildingOS and
offers monitoring and aggregated data global costs. Although it shares the expected to DAREED monitoring
Dashboard
from meters, lighting and plug load ideas and main features of DAREED, it and it could be a serious competitor
controls. It also counts with on-site might not cover design expectations as to tailor-made software. In general
generation
utilities
to
provide
a well as a tailored solution.
38
it
covers
the
requirements
Deliverable: D 2.5
Organisation: UBRUN
complete understanding of the building Updates and functional modifications associated
under study. It was designed as a tool implies depending on third-parties.
to
the
Monitoring,
Analysis and Control tools.
for minimizing costs and optimizing As it happens with others existing tools Besides monitoring, tool’s analysis
energy bill, under analogous premises the integration of this market software characteristics already included in
as DAREED. Predictive forecasting is may be complex and require great the
software
employed to notify incoming peak effort to combine it with other tools in comprehension
demand. Automatic utility bills analysis the platform.
and
comparisons
as
well
as
performance reports are able to be
created.
Notifications are sent to users in case a
communication
error
occurs
and
meters do not report data. BuildingOs
provides
regression
consumptions
comparing
and
past
analysis
trend
and
for
analysis
predicted
performance data. Lucid’s Dashboad
complements BuildingOs and offers
demand-side
management
features.
The application is compatible with
39
behaviour.
ease
of
the
building’s
Deliverable: D 2.5
Organisation: UBRUN
many hardware brands in the market.
Its design was tailored to several types
of users as managers, executives,
tenants and visitors in the building,
service providers and public disclosure.
Different
inspected
buildings’
data
together
could
and
be
make
comparison between them.
Dexma’s DexCell
As the previous tool, this manager It might be more expensive than the To DAREED it could substitute the
Energy Manager
covers practically all the necessary tailored-made version and difficult to components in charge of energy
functionalities required for a single integrate with other sections existing in monitoring and analysis features for
point: DAREED. It is limited to one building a single building or a set of them
consumption-generation
monitoring, data aggregation, trends so for those users who are interested managed as an individual. Control
and
predictions,
automatic
cost
management.
cost
reports
analysis, on knowing neighbourhood or precise must
or
be
alerts areas’ information and trends the independently.
isolated use of this tool will not be
sufficient
and
it
will
require
a
complementary tool which aggregates
data from different buildings to create a
global scene. It neither has control or
40
implemented
Deliverable: D 2.5
Organisation: UBRUN
distributed generation features, so any
desired interaction with the devices
need
to
be made through
other
channels.
C3 Energy
It is a very user-friendly software that is As a web-based software as a service It’s
provided in three scales, addressed to product
big
enterprises,
companies
it
suffers
from
user-friendliness
and
the
security division in three scales to target
or concerns because it enables to extract different stakeholders requirements
residential sector. Its main features are data from any device but without might help to design DAREED
energy analysis, energy benchmarking offering any controls. This tool lacks of modularity while keeping all its
and
planning.
It
offers
different a long experience and that might capabilities.
categories to aggregate and compare create
certain
concerns
on
its
consumptions: by key performance performance and efficiency. In contrast
indicators, time, facilities, energy use or to its user-friendliness, it lacks some of
hourly spending. C3 Energy also helps the more sophisticated tools provided
understanding how weather influences by other tools.
on energy usage and provides users
with tailored energy saving tips.
eSightenergy
Entirely web-based focused on small Basic verifying billing data tool which Its most useful feature for DAREED
businesses and large enterprises. It does
not
provides
any
other could be the conjunction of energy
focuses more on contracts and tariffs associated service unlike other tools in contract analysis with energy use
41
Deliverable: D 2.5
Organisation: UBRUN
than on energy use but still provides the market. Data handling might not and its advance features in bill
energy meter analysis, performance offer enough security for some users.
analysis and budget forecasts.
analysis or baseload analysis. It has a
broad set of alarms to inform of missing
data, deviations or exceptions.
EnergyCAP
Long life of experience and many Complex features less user-friendly Its main feature is its flexibility.
different features as reducing billing than
errors,
improving
budgeting
in
other
applications.
The Energy
analysis,
bill
audits,
or software works only on Windows and benchmarking utilities and reports
identifying energy inefficiencies. It is has regular updates. It is focus more are general and powerful so they
offered as a traditional software, a web- on energy use than in billing tracking cover a great variety of cases.
based and a cloud-based system and analysis.
depending on the business marketed.
Weather data could be analyse to
inspect its impact in energy use.
Table 4: Evaluation of Existing tools relevant to DAREED Consumption Monitoring, Analysis and Control Component
As summary, the following table relates the introduced tools with those services associated to Monitoring, Analysis and Control in DAREED.
It offers an overview, showing which services are covered and could be externalized and which cannot. In general, there are many
applications for monitor buildings’ consumption but few that could manage a set of buildings or a whole area. This fact establishes DAREED
solution at a higher step and emphasises the ambition of the project.
42
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Tool name
S08
S9
S10
S11
The OLW’s
X
X
X
Powertracker
X
X
X
X
X
X
eSightenergy
EnergyCAP
Lucid’s BuildingOs
S12
S13
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Dexma’s DexCell
C3 Energy Customer
Analytics
X
S14
S15
S16
X
Table 5: Existing tools mapped against services provided by Consumption Monitoring, Analysis and Control
Note that none of these commercial tools cover the services related to optimize the control of distributed generation systems, more
appropriate of an SCADA or other specific applications.
4.3 Energy Management
Regarding energy management at district and local level as it has been defined in this project; there is less commercial software that could
cover the features assigned to the Energy Management tool. While for monitoring and analysis there exist a broad variety of solutions
covering different categories towards characterizing and evaluate energy use at buildings or a set of them, for these other services we
43
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Organisation: UBRUN
encounter that solutions focus on certain aspects without considering others. For example, one of the tools included in the previous section,
BuildingOS and Dashboard, manage to cover demand-side management and users’ implications, and manage peak load problems too.
Existing Tool
Lucid’s BuildingOs and Dashboard
Benefits
Costs/Limitations
BuildingOs
together
Dashboard
are
provide
an
able
Service Contribution
with Even though this software has many Its ideas to make building’s
to valuable tools designed to manage users actively participate in
energy energy use and reduce building’s energy demand reductions
management
tool
combines
the
actions
managers
and
users
that environmental impact, it lacks of the could
be
Dashboard
on provide optimization services as the citizens’
several
encourage
consumption. consumption
counts
widgets
with parameters
based
and
tariffs
consumers imply cost reduction.
challenges
on
comfort
or
offer
to different available tariffs that could
reducing their energy use. It
provides
to
of necessary capabilities in order to DAREED in order to create
awareness
buildings to reduce their execution of automatic rules, manage energy use.
energy
applied
and
open discussions to share
best practices. Besides, it
44
on
Deliverable: D 2.5
Organisation: UBRUN
provides
a
library
sustainable
carried
of
initiatives
out
in
buildings
under management.
US Department of Energy’s BEopt
BEopt
software
capabilities
to
provides It is meant for optimizing just one Its library of measures and
evaluate building and its capabilities might not cost for use across analysis
residential building designs meet every user’s requirements.
and
identify
efficiency
as well as its detailed site
cost-optimal Regarding user-friendliness, it lacks characterization
packages.
define
a
It of smart and rich visual contents of suitable model that could be
bases its actions on utility other applications. In fact, it relays on applied to DAREED.
rates,
schedules
and complementary software known as
demand response to critical Dview
for
events. Besides optimization simulation
features
it
provides drawback
simulation-based analysis.
Toshiba’s CEMS
The
Community
visualizing
output.
is
that
An
it
hourly
important
requires
a
complex modelling of the building.
Energy It
relies
on
complementary The integration of distributed
Management System tries to management systems as BMS to generation
combine the management of receive information from sites and their
the
supply
of
systems
and
management
in
power, control. Its energy rates management conjunction
to
energy
including renewable energy services are not as evolved as in demand follows DAREED
45
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Organisation: UBRUN
sources
with
demand. other applications.
Building
and
factories For
energy
objectives
and
could
be
local optimization there are used as reference.
management several specific solutions depending
systems are manage and on
the
type
of
building
operated together through (residential/factory). It is not clear the
the CEMS. It implements compatibility with other brands local
energy
supply-demand management equipment.
predictions to estimate loads
and control them.
Table 6: Evaluation of Existing tools relevant to DAREED Energy Management Component
The following table enumerates the services covered by the previously introduced tools providing a better scope of each application’s
functionalities.
Tool name
Lucid’s
BuildingOs
S17
S18
S19
S20
X
S21
X
and
Dashboard
46
S22
S23
S24
X
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Organisation: UBRUN
BEopt
X
X
Toshiba’s CEMS
X
X
X
X
X
X
X
Table 7: Existing tools mapped against services provided by Energy Management
4.4 Decision support and energy awareness
4.4.1 Decision Support Tools and Projects
In this section, we will provide a brief overview of projects and software related to decision support for improving the energy efficiency in a
urban context.
Existing Tool
Benefits
Costs/Limitations
Service Contribution
The tool has been realized with the financial The module for analysis of The tool may provide ideas for data
support of one of the German energy the
street
providers by Open Experience GmbH and consumption
LightMasterTool
KIT (both partner in the DAREED project). It integrated
lighting
could
without
total acquisition of district consumption
be related
data
any decision
and
support
supports local decision makers to consult additional costs. However it methodologies
what is the energy saving potential of the is on a more general level cumulated
data
the
DAREED
tool
regarding
for
in
calculating
the
future
street lighting system of their towns? The tool than required in DAREED (regarding predicted consumption).
47
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is web-based and provides a real-time and do not provide building Gathered know-how on integrating a
simulation of different parameters (product, consumption analysis.
catalogue with real products (existing
concrete city street, requested period for
on the market) for street lighting or
amortization etc.) that the user can select and
RES technologies could be also
configure following its own requirements in
used in the DAREED approach.
order to answer the question: how much
electricity and costs we could spend if we
change our lighting system with exactly this
product from this producer?
“Nachaltige
Kommune”
(Sustainable The
is Even if the building perspective
platform
municipality) (realized by Open Experience conceptualised
for
a lacks, the experience gathered in the
GmbH and KIT, both partner in the DAREED municipality with a range of Nachaltige Kommune and some of
project) is a software prototype for decision installed technologies for their modules could be used for an
support system at municipality level. The energy
Nachaltige Kommune
project idea and some of the main software assumes
production.
a
It interactive
map
visualization
of
comparison energy performance, selection of
backbones (modules) are based on the between the energy use technologies and data acquisition
European
CONCERTO
Premium
project pattern of different areas techniques.
results.
(industrial, residential etc.),
The Nachaltige Kommune aims to present therefore
is
not
very
transparently and understandable what is the suitable for district level,
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current total energy consumption (structured where detailed analysis on
by sectors (industrial use, residential use etc.) building pattern should be
and type of energy use) and production performed.
(structured by type of technology, Photovoltaic,
Thermal energy etc.) in a municipality, that
support an efficient monitoring and fast
overview of the energy balance. Second the
platform could be used as a decision-support
tool for initial check on what will be the impact
of the use of a specific RES on the energy
balance of this particular municipality.
EEPOS Project (FP7)
The EEPOS Project (Energy management No
recommendation The engagement tools in EEPOS
and decision support systems for Energy systems. Limited predictive share some similarity with the citizen
POSitive neighbourhoods) aims at reducing tools.
engagement
the dependence of selected neighbourhoods
services from WP5 and may provide
on the external grid. The project plans to
ideas for their development. Besides
achieve such goal by several means and in
that, the similarities with DAREED
particular by leveling peaks via automated
WP5 services are quite limited.
load shifting and by exploiting differences on
electricity usage patterns (e.g. households vs
49
and
involvement
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offices). The project adopts a two-layer
architecture, with local systems focused on
load control and a central system for
monitoring and coordination. Additionally, the
central system acts as an aggregator for the
energy market, thanks to specially developed
brokering tools. So called "engagement tools"
(including web forums, reporting systems,
and energy saving games) are offered to
encourage
load
shifting
among
the
neighborhood inhabitants.
RETScreen
RETScreen is a Canadian-made software Focus on single buildings. RETScreen 4 has several similarities
suite
that
allows
to
quickly
evaluate No
recommendation with S28 of DAREED WP5: as a
("screen") the economical viability of many system
main difference, RETScreen 4 is a
Renewable-energy
Energy-efficient
predictive analytics tool, suitable for
Technologies (RETs). The suite is mainly
evaluating user-designed scenarios.
designed for building managers and consists
Conversely, S28 will provide actual
of two tools: RETScreen 4 (for investment
recommendations,
planning)
(for
account complex constraints, budget
monitoring the performance of an installed
limits and the user preferences.
and
and
RETScreen
Plus
50
taking
into
Deliverable: D 2.5
Organisation: UBRUN
system). The software comes with a very
Despite this, the richness of the
large database of technologies, including
RETScreen database could prove
renewable energy generators but also energy
invaluable
efficiency measures (e.g. building insulation).
development
Additionally, the database includes models to
DAREED services, provided the data
assess the performance of each technology,
can be accessed.
to
of
speed
S28
up
and
the
other
plus worldwide weather information.
Market VuePoint
The Market VuePoint suite, by VuePoint Different
aims.
No The suite is somehow related to the
solutions, provides access to up-to-date recommendation system
DAREED
information about the energy and gas market.
(since the energy market has a
The suite falls into the descriptive and (in
strong impact on tariff prices and
part) predictive analytics class, since it offers
revenues for the energy providers),
tools to analyze market information and to
but the relation is not very close,
perform risk assessment, allowing the user to
since the tools have very different
make informed decisions.
aims.
EnPROVE Project
The focus of the EnPROVE project (energy Focused
on
(FP7)
consumption prediction with building usage buildings.
May
decision
support
tools
single The project (now in its final stage)
lack has several connections with S28
measurements for software-based decision scalability.
and it may be possible to exploit
support) is on improving the energy efficiency
results from EnPROVE in DAREED.
of a single building. The project employs a
However, this must be done with
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wireless sensor network to obtain fine-grain
some care due to the big difference
data about a target building; predictive
in
models are used to estimate the building
districts):
consumption when certain energy efficiency
EnPROVE models may prove to be
improvement are made; finally, an expert
too fine grained to be effectively
system allows to automatically select a set of
employed
promising scenarios, that are then ranked
related decision-making techniques
according to multiple criteria and presented to
may have insufficient scalability.
scope
(single
because
in
buildings
of
DAREED
this,
and
vs
the
the
the decision makers. The EnPPROVE project
covers (to some degree) all the spectrum of
business
analytics
techniques,
from
descriptive to prescriptive.
goal
of
the
OPTIMUS
project The project is in its early OPTIMUS is in its early stages,
OPTIMUS Project
The
(FP7)
(OPTIMising the energy USe in cities with stages. No tools to define which makes it difficult to identify
smart decision support systems) is to provide incentive schemes.
specific results to be integrated in
local authorities with on-line tools to devise
DAREED, but the two projects are
(and monitor) city-level energy plans. The
definitely related and hence some
project focus is on integrating data from
kind
heterogeneous domains, namely weather
advisable.
conditions, social mining, buildings energy
52
of
interaction
would
be
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profiles, energy prices, energy production.
The integration is enabled by semantic
technologies.
The
project
includes
the
development of tools for automatic scenario
recommendation, but the design of incentive
and regulation schemes for the actual
implementation of such scenarios is left to the
policy maker.
BESOS Project (FP7)
The focus of
BESOS (building
energy No recommendation tools. The project falls mostly in the
decision support systems for smart cities) is Emphasis
on the development of integration technology and
not
on
on
integration descriptive analytics class and is
analysis, therefore only loosely related to
to allow data sharing and communication assessment, planning.
DAREED WP5.
between traditionally separated systems in a
urban context (e.g. lighting, heating). This
integration layer enables the development of
higher-level applications for monitoring and
analysis tasks.
TRANSFORM
The TRANSFORM program (transformation No
recommendation The scope (city level) and target user
Program (FP7)
agenda for low carbon cities) includes the system.
No
development of a decision support tool, which designing
53
tool
for (local
authorities)
of
the
incentive TRANSFORM program are quite
Deliverable: D 2.5
Organisation: UBRUN
is currently in the early stages. The tool will schemes.
The similar to those of DAREED; hence
allow local authorities to assess the effect of development is in its early some kind of interactions between
multiple measures on the CO2 emissions of a stages.
the
target city. This goal will be achieved by
predictive models) is likely to be
integrating
beneficial.
analysis
and
simulation
projects
(e.g.
sharing
of
techniques operating over geo-tagged data.
Similarly to the OPTIMUS
project,
the
TRANSFORM decision support tool will allow
the local authorities to identify promising
improvement
plans,
but
the
design
of
incentive schemes and other measures for
the actual plan implementation is left to the
policy maker.
CItInES Project (FP7)
Two
decision
support
tools
are
being Crystal
City:
developed within the CitInES project (city and recommendation
industry energy strategy), called Crystal City No
and
Crystal
Industry
and
tool
targeting incentive
for
Sustainable Energy Action Plans and to industry
54
recommendation
system
in
system. Crystal Industry is similar in spirit the
designing one we plan to develop in DAREED,
schemes. but
respectively city authorities and industries.
The Crystal City tool allows to monitor local Crystal
No The
the target
domain is quite
different (industry plants vs urban
Industry:
scope
Single districts). Conversely, Crystal City
(too targets
the
same
domain
as
Deliverable: D 2.5
Organisation: UBRUN
identify (via simple simulation techniques) different from DAREED)
DAREED, but it lacks prescriptive
promising actions to improve the energy
analytics capabilities.
efficiency of city districts. The Crystal Industry
tool allows to monitor the energy behaviour of
existing industrial plants, to assess the
potential impact of technological upgrades or
of changes in the usage strategy of the
existing
equipment,
and
finally
to
automatically obtain recommendations (via
optimization
techniques)
about
how
to
improve the current usage strategy.
UMBRELLA (FP7)
The UMBRELLA project (business model Single building scope. The The project (in its early stages) falls
innovation for high performance buildings project
is
in
its
early into the prescriptive analytics class
supported by whole life optimization) aims at stages.
and,
supporting
optimal
scope, it is definitely related to
business models for improving the energy
DAREED WP5. In particular, an
efficiency of a target building. In the project
interaction between the two projects
terminology, a “business model” refers to a
may
combination of 1) one-time actions to improve
predictive models for the building
the building efficiency (e.g. installing PV
consumption and 2) optimization
the
identification
of
55
despite
occur
the
at
the
single-building
level
of
1)
Deliverable: D 2.5
Organisation: UBRUN
plants) and 2) management strategies for the
technologies.
installed equipment (e.g. heating strategies,
resorting to ESCOs).
INDICATE Project
The goal of the INDICATE project (indicator- The project is in its early The project is definitely related to the
(FP7)
based
interactive
decision
support
and stages
and
focus
on DAREED support system and some
information exchange platform for smart building energy efficiency. interaction may occur at the level of
cities) is the development of a decision Emphasis.
Limited predictive model and (perhaps) of
support system to assist the definition and recommendation
system. optimization
management of city-wide energy efficiency No
tool
for
technologies.
As
in
designing many other cases, the system allows
improvement plans. The project has an incentive schemes.
the identification of best practices,
emphasis on the design of impact indicators
but
and the use of simulation to assess the effect
recommendations about how they
on the indicators of specific decisions. The
should be actually implemented (e.g.
project will also feature a recommendation
incentive schemes).
does
not
system that should be able to suggest best
practices and to identify the most effective
improvement to apply to specific building or
systems.
Table 8: Evaluation of Existing tools relevant to DAREED Decision support and Energy Awareness Component
56
provide
Deliverable: D 2.5
Organisation: UBRUN
Schematic summary of the possible interactions with DAREED services:
Tool name
S25
S26
S27
LightMasterTool
limited
X
Nachaltige Kommune
limited
X
EEPOS DSS
RETScreen
X
Market VuePoint
EnPROVE DSS
X
OPTIMUS DSS
X
limited
limited
X
limited
limited
UMBRELLA DSS
X
limited
limited
INDICATE DSS
X
limited
limited
BESOS DSS
TRANSFORM DSS
Crystal City
Crystal Industry
Table 9: Existing tools mapped against services provided by Decision support
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4.4.2 Awareness and Involvement Projects and Tools
In this section, we discuss existing projects and tools related to increasing citizen awareness and involvement with respect to energy
efficiency issues.
Existing Tool
Benefits
Costs/Limitations
Service Contribution
BeAware Project
The project BeAware has as the goal to No
(FP7)
develop applications that engage users to system.
the development of WP5 with regard
adopt
to
virtuous
energy-saving
behaviours
recommendation The project can support DAREED in
consumer
awareness
and
through interactive visualisations and games.
engagement and in services 28 and
The project developed the EnergyLife mobile
29.
interface, that incorporates lessons from
environmental
intervention
appliance
environment
decreased
psychology
to
relay
sensors,
that
electricity
and
feedback
information
offering
rewards
a
from
gaming
users
consumption.
for
The
provided feedback consists of information
about
the
consequences
of
household
actions that involve electricity consumption.
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There are two basic types of feedback:
consumption/saving information and smart
advice tips. The project uses metaphors that
embed consumption information into the daily
routines of the consumers. In addition, it
provides
community
tools
that
support
competition, discussion and reflection inside
the household and with the community of
consumers.
E3SoHo Project (CIP)
The project E3SoHo (Energy Efficiency in Web-based only
The
European Social Housing) implemented pilots
awareness and engagement of the
on smart energy solution for social housing,
energy
by raising customer awareness through
contribute in DAREED in WP5 and in
feedback on consumption, training tenants
services 28 and 29.
and building owners on energy efficiency and
offering personalised advice for improving
their energy efficiency, reducing the energy
consumption and increasing the share of
RES. The ICT solution developed in the
project provides access to all the relevant
59
projects
covers aspects
consumers
and
it
of
can
Deliverable: D 2.5
Organisation: UBRUN
information
through
a
information
consumption
about
energy
Web-based
consumption
interface.
The
provided
includes:
energy
profile
(current
energy
consumption, real time, daily report and
historical data), real time energy cost, source
of the energy use, carbon footprint related to
the
energy
performance,
general
and
personalized recommendations towards ecofriendly
behaviour,
on-line
personalized
simulations of the savings that they would get
if changing their behaviour, information about
the best time to use electronic appliances,
comparison of the energy performance of
their building against the energy performance
of other similar buildings.
Efergy Engage
Efergy is a global manufacturer of energy Closed standards
The Engage Platform is relevant to
Platform
monitoring systems. The latest commercial
some aspects of DAREED with
offering refers to the Engage Platform, an
regard to consumer awareness and
online
engagement. The examination of
platform
that
shows
energy
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consumption online. The development is
commercial products may support
based on the principle that by making energy
the potential commercialization effort
consumption visible, it supports engagement
of the DAREED outputs. Note that
and energy savings. The Dashboard is
several similar commercial products
accessible through Web browser, smart
have been developed recently.
phone and tablets and helps monitoring and
managing energy in real-time, as well as
understanding energy consumption habits.
Engage Platform users participate in a
community that helps them view their usage
against similar homes and see how they are
doing. The community provides opportunities
to share, compare, compete and learn from
the others, as well as incentives to improve
energy saving scores.
S3C Project
The project S3C (Smart Consumer, Smart No outcomes delivered yet
The
Customer,
on
development and the objective is to
consumer awareness and engagement in
develop an interactive toolkit. If they
smart energy solutions. The aim of the
deliver research outputs soon, these
project is specifically to develop ready-to-use
outputs can be taken into account in
Smart
Citizen)
focuses
61
project
is
still
under
Deliverable: D 2.5
Organisation: UBRUN
tools for long-term end-user engagement by
the DAREED project.
addressing the end-user in his three roles as
smart consumer, customer and citizen.
Table 10: Evaluation of Existing tools relevant to DAREED Decision support and Energy Awareness Component
Schematic summary of the possible interactions with DAREED services:
Tool name
S28
S29
BeAware
X
X
E3SoHo
X
Efergy Engage Platform
X
S30
X
S3C
Table 11: Existing tools mapped against services provided by Decision support and Energy Awareness
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4.5 Existing relevant tools mapped against DAREED components
As a summary, in the following table the most relevant tools analysed in the previous sections are
mapped against the DAREED components. The table offers an overview showing a schematic
summary to highlight which tools could be integrated in DAREED platform for each component.
The actual convenience of either integrating the tool identified or not will be evaluated in the
implementation phase, depending also on the technological choices that will be made for the
DAREED platform.
DAREED Components
Existing Tool Name
EnergyPlus –
Modelling and
Simulation
Consumption
monitoring,
analysis and
control
Energy
management
Decision
support and
energy
awareness
X
simulation engine
DOE-2 – simulation
X
engine
Lucid’s BuildingOS and
X
Dashboard
Dexma’s DexCell
X
Energy Manager
C3 Energy
X
eSightenergy
X
EnergyCAP
X
US Department of
X
X
X
Energy’s BEopt
Toshiba’s CEMS
X
RETScreen (only for
X
models)
BeAware Project
X
Efergy Engage
X
Platform
Table 12: Summary of Existing Tools mapped against DAREED Components
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With regards to the Modelling and Simulation component, it will be decided during the
implementation phase which simulation engine is to be used between EnergyPlus and DOE-2.
Besides it is necessary to emphasize that each of the identified tools can be used only for a single
building. As a result, to integrate them into the district concept, they must be adapted to represent
the whole district as conceived in the DAREED project, and not just a single building as they were
designed originally.
In terms of the Monitoring, Analysis and Control component, there is an extensive catalog of tools
existing in the market which include many of the desired functionalities to be developed. This fact
might raise the following question; why not take advantage of these existing solutions? A full or
partial integration of an already existing tool might appear to provide clear advantages in the
construction of DAREED. However, any integration must be carefully evaluated and analyzed
during the actual implementation, as it occurs with the other components, and of course, benefits
must overtake integration costs. The majority of the founded solutions, Lucid’s or Dexma’s tools
to name a few, offer solutions that are in line with the DAREED objectives although in the
evaluation of a district scope their capabilities are slightly limited, or even non-existent, not to
mention the necessary management associated with licensing issues. In relation to the Energy
Management component, the same concern could be found. The main disadvantages of
integrating an existing tool are the intended scope for a district and the eventual support of certain
solutions on secondary applications or services which could increase the difficulty towards its
integration. Thus, any integration of an existing solution for these components could be
discarded.
Most existing systems are weakly related to the decision support tools to be developed in WP5,
for two main reasons: first, all the existing tools lack prescriptive analytics capabilities, which is
the fundamental focus of T5.2 and T5.3 in WP5. Second, most of the existing tools are designed
with single buildings in mind rather than whole districts, hence they require fine-grained
information (which may not be available for big urban areas) and make often use of detailed
models (which may have poor scalability). As a result, integrating existing tools in the decision
support component from WP5 is not likely to be beneficial. However, we do plan to exploit
techniques and (in particular) models from other tools, in case they prove scalable enough to be
employed at district level. The RETScreen software seems particularly promising from this point
of view.
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Finally, it is worth to note that although existing tools are not capable of prescriptive analytics,
some systems currently under development in other research projects will have such features.
Hence, stressing the importance of establishing interactions between DAREED and other projects
with similar scope or goals.
5. Trade-off and integration analysis of existing energy
management tools/solutions
Through the previous sections, several commercial platforms available in the market have been
studied evaluating their principal features in order to create a map of services in consonance with
the DAREED objectives. Consumers have at their disposal an extensive catalogue of tools to
select those that would satisfy their needs concerning energy efficiency. From that collection of
competitors, we have focused our research on a small group that offers features close to the
functionalities of DAREED. As DAREED application tends to encompass services from many
different areas (as monitoring, simulation, analysis, modelling, advising, control or awareness), it
becomes challenging to find a single existing tool that corresponds with DAREED in its many
facets. On the other hand, if the examination is limited to a particular field or functionality, then we
are able to identify software platforms that might compete with a future use of DAREED. In other
words, users will probably not utilise every functionality provided by DAREED in their average
daily use and in this case, they might prefer to choose a more modest platform that fits their
requirements more precisely even though that would cause to renounce to additional features.
Following this line of thought, in case the DAREED platform was supported by other independent
software, this would require additional integration tasks in order to provide communication among
DAREED’s internal components and the external software. Otherwise, both systems would not be
able to interact, share information and work together towards fulfilling user’s goals. Particularizing
this fact on an example, if monitoring services were not covered by tailor-made software but for a
commercial one, this ought to be integrated with the rest of the platform in order to share the
retrieved data for other components to utilize. Regardless of the purpose or even the
effectiveness of this external tool, if it cannot exchange information it cannot be used.
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Most of the tools that have been enumerated in this deliverable, lack in the possibility of
integration as they are marketable and are designed to be used just with certain products
associated to its brand or under certain conditions. For instance, Dexma’s Energy Manager
software befalls to be marketed under several names by the companies in the partners program.
The same features and appearance could be found in Dexma’s, Current Cost’s Control or Solar
Tradex’s ST Energy Manager to name a few. Besides, regardless of the complexity of the
integration, using commercial software most probably implies purchasing licences or patents
which will increase development costs.
Therefore, in case the integration is not a valid option, a conservative strategy would be to take
the services and features of these tools as a reference in order to design a baseline from which to
start configuring DAREED solution. This reference would be the minimal features and services
desirable to be provided and to create a competitive and resourceful tool.
In summary, the outline of the tool and its desirable features can be described by answering the
following two questions:
•
How will DAREED compete with existing solutions?
DAREED solution is designed to be a competitor to any other existing tool in the market as it
comprises in a same platform a more complete set of functionalities which gives it a greater value
to its potential users, without adding unnecessary complexity or neglecting user-friendliness.
DAREED will be a solution that combines a building (and building blocks) energy
management system with a district one, which is a unique approach with high technical
complexity taking into account the different granularity of data and data acquisition techniques.
•
If the integration of existing tools involves high costs and great effort, which of the desired
functionalities present in those existing tools should be considered in DAREED?
Concerning small and domestic users interested in using the platform for improved their energy
efficiency the key principle will be simplicity:
-
Plug and play technology and minimum hardware installation
User-friendliness
Turnkey solution ready to install
For larger and more advance users, the key will be its full set of complete capabilities making it a
resourceful tool and a tough competitor in any case:
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-
Forecasting services
Utility bills analysis
Performance reports
Advance notification services
Trend analysis
Demand-side management
Scalability
Compatibility
Appearance and features adapted to user’s role
Cost and contracts analysis
Automatic reports
Alerts and notifications
Control of certain loads
Building modelling and characterization
Benchmarking
Key performance indicators
Budget forecasts
Library of measures
Management of distributed generation
6. Conclusions
Throughout this D2.5 report, relevant existing tools and previous projects have been analysed,
providing a state of the art on available best practices of simulation, energy consumption analysis
and control, energy management and decision support.
For each of the identified DAREED components and the services in D2.3 and D2.4, existing tools
and solutions have been considered, with its benefits and limitations, and an integration analysis
has been provided, in order to evaluate their exploitation in the components of the DAREED
system. Finally a brief trade-off and integration analysis of the existing energy management
tools/solutions against DAREED has been reported.
The results obtained in the present report will be used in subsequent tasks, in particular in WP3
for the “Modelling and simulation tool”, in WP4 for
“Consumption
monitoring,
analysis
and
control tool” and “Energy management tool”, and in WP5 for “Decision support and energy
awareness tool”.
67
Deliverable: D 2.5
Organisation: UBRUN
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72