CONTENTS
COMPANY DESCRIPTION
2
Vision statement
2
Goals
2
INDUSTRY
2
Industry Overview
2
Industry Growth and Outlook
2
Industry Competition
3
SOLUTION AT A GLANCE
4
DETAILED SOLUTION
5
USE CASE OF ENERGY ANALYSIS BY IoTeM
10
MARKET SIZE
11
BARRIERS TO ENTRY
12
THREATS & OPPORTUNITIES
13
SWOT Analysis
14
COMPETITION
16
Competitive Analysis Worksheet
16
TARGET CUSTOMER
17
REQUIREMENTS - DATA & PEOPLE
18
PRODUCT DEVELOPMENT ROADMAP
19
Version-I Initial release.
20
Version-II Platform upgrade with Machine learning and AI features
21
Version-III Platform Enhancements
22
Future Road Map.
22
EXPANSION PLAN
23
BUSINESS PLAN (FINANCIALS)
24
EXPENSES
24
REVENUE
25
RoI
26
RISKS
27
SEAM – Smart Energy & Asset Management
COMPANY DESCRIPTION
Vision statement
To provide a state-of-the-art IoT platform to the market, one that uses an autonomous
artificial intelligence-based technology for the optimization of the energy consumption of
HVAC system in buildings.
Goals
1. Short Term

Build and launch the cloud based IoT platform in the UAE.

Complete Pilot project to effectively manage the thermal equilibrium of a
building using dynamic modulation and continuously optimize energy flow to
ensure occupant comfort and maximum energy efficiency.
2. Long Term (>5 years)
a. Being a cloud-based platform, the business can be expanded across the Middle
East and South East Asian Region.
b. Include more AI modules in the platform to create automated fault detection
and diagnosis in MEP assets to help in generating predictive maintenance alerts
which in turn increasing the efficiency and asset life cycle.
INDUSTRY
Industry Overview
Today, building owners and facilities managers have increasing pressure not only to reduce
energy and operational costs but also create business value for their buildings and the assets
within. Most of the current building control systems in use today are designed with an
outdated server client systems architecture.
This makes the cost of the integration and data management of the systems a barrier to most
organizations which prevents implementation of innovative cost reduction measures,
creation of operational efficiencies, or data analytics-supported identification of new revenue
streams.
Industry Growth and Outlook
In recent years, the technology, standards, and expertise required to maximize facility
efficiency have matured. There are recent technological advances in integrated circuits and
SEAM – Smart Energy & Asset Management
Page 2
SEAM – Smart Energy & Asset Management
wireless communications that made efficient, low-cost, low-power devices use in remote
sensing applications available. The synergy created by that convergence is translating into
lower support costs along with increased efficiencies in operations and energy usage.
Advancements in the development of technology, standardization, and expertise combined,
unleashes opportunities like never for Enterprise and IT Managers to improve building,
workspace, and thereby business performance.
We foresee the silos among departments will start to break down as technology becomes
more accessible, easy, and intuitive to use. With the advent in IoT technologies, the solutions
for buildings are wide-ranging. Here are a few examples:
Energy optimization
Building network control
Connected Security Monitoring
Workplace optimization / workforce engagement
Industry Competition
The internet of things (IoT) market is highly competitive owing to the presence of many large
and small players in the market operating in the domestic as well as in the international
market. The market appears to be fragmented due to the presence of many technological
giants in the market. Key strategies adopted by the major players in the market are product
innovation and mergers and acquisitions. Some of the major players in the market are Cisco
Systems, Inc., Google, Inc., IBM Corporation, Microsoft Corporation, among others.
March 2020 - Microsoft & Cisco Systems announced a partnership to enable seamless data
orchestration from Cisco IoT Edge to Azure IoT Cloud.
January 2020 - Cisco introduced an IoT security architecture that supplies enhanced visibility
across both IT and IOT environments and protects processes.
April 2019 - IBM Corporation announced a collaboration with Sund & Bælt, which owns and
operates some of the largest infrastructures in the world, to assist in IBM's development of
an AI-powered IoT solution designed to help prolong the lifespan of aging bridges, tunnels,
highways, and railways.
As with any recent technology, IoT arrives with challenges. Lack of standardization results in
interoperability challenges. Different vendors have their own proprietary platforms and
solutions, and these are usually not interoperable. This means that the costs are higher, and
SEAM – Smart Energy & Asset Management
Page 3
SEAM – Smart Energy & Asset Management
the buyers and users bear a lot of risk. There is also the risk of potential redundancy. You
might adopt a technology that eventually loses to an alternative and ends up redoing
everything.
SOLUTION AT A GLANCE
The platform enables the collection of data from many disparate systems and protocols from
any manufacturer, including HVAC, Fire, Security, Lighting to simplify the management and
value of those systems. In addition, the platform integrates enterprise systems data, which
enables the automation of routine building maintenance activities.
AI is used to predict how the flow of energy will evolve over time and if it predicts an
unwanted thermal event in the future, to adjust the HVAC system to eliminate that event
before it happens.
In addition to deciding which adjustments are best for ensuring occupant comfort, AI can
also evaluate the optimal HVAC system configuration required to achieve greater energy
efficiency, thereby saving money, and making buildings greener by reducing the load on the
power grid.
SEAM – Smart Energy & Asset Management
Page 4
SEAM – Smart Energy & Asset Management
DETAILED SOLUTION
The deployment will be done in 2 phases.

Create and launch the infrastructure for data collection and data processing.

Once the data is available introduction of Energy Management AI algorithms and
creation of front GUI and dashboards.
Infrastructure
The first step in bringing a building to life is developing the foundation, for connectivity
analytics, and sustainability. Data Collection is done by integrating the existing Building
management Systems over open communication protocol to record the HVAC parameters
both previous and current live data. The data captured from the various systems are
transferred central server over secured VPN Tunnel and is represented in a rich and intuitive
Graphical user interface.
The infrastructure includes remote site data collector to central servers for database and
application software’s.
Data Collector- Gateway device that provides connectivity to meters and building automation
systems to extract data for the platform.
Central Server cluster- the data will be hosted on a cloud-based environment which include
the below configurations.
Application data server
Database cluster
Graphics DB servers
API servers for integration
File servers etc.
Calculating a building’s thermal energy equation is the first step towards optimization and
requires calculating its energy leak rate and power-to-thermal load relationship. The
process involves the below steps.
SEAM – Smart Energy & Asset Management
Page 5
SEAM – Smart Energy & Asset Management
Site setup
Site setup is first step of the site provisioning. The user must go to space module and define
the space hierarchy. Space hierarchy shall be definition of the facility, building and floor with
relationship.
The space provisioning shall ask for facility level information like
1. Name of the space.
2. Postal address.
3. Built up area.
4. Longitude and Latitude.
5. Local units and currency reference.
Meter setup
Meter setup is the meter provisioning. Meter provisioning allows to define the meter
relationship between meters within the space. Meter definition will allow user to associate a
meter with a space and Meter definition will also allow user to set up one level meter
relationship.
Meter provisioning shall ask for the information like
1. Meter name.
2. Type of measured commodity.
3. Type of measure load.
4. Type of meter.
5. List of energy parameters measured.
Equipment setup
Equipment setup is the equipment provisioning. Equipment provisioning allows to define
the equipment relationship and equipment within the space. Equipment definition will allow
user to associate an equipment with a space and equipment definition will allow user to
establish equipment to equipment relationship.
Equipment provision shall ask for the information like
1. Equipment name.
2. Equipment category.
3. Equipment sub type.
4. Space equipment belongs.
5. List of points under the equipment.
SEAM – Smart Energy & Asset Management
Page 6
SEAM – Smart Energy & Asset Management
Faults detection and Diagnostics setup
FDD setup is defining the fault detection and diagnostics rules. FDD set up allows to define
the equipment rules and sub sequent diagnostics. The rule definition module allows user to
define the logical/mathematical equation. The defined equation shall be linked to an
equipment or multiple. This module also allows user to define the threshold limits and link
them to individual equipment’s.
Baseline
Baseline is defining the energy benchmark. Baseline setup allows user to create multiple
baselines. Each baseline shall allow user to add baseline by each year each month.
Schedule
Schedule setup is defining the schedule. The schedule allows user to define the schedule
and link it to the calendar. The user can self-define multiple schedules.
User
User setup is defining the application users. The user module allows to define the users and
assign the permissions.
SEAM – Smart Energy & Asset Management
Page 7
SEAM – Smart Energy & Asset Management
Energy Management Module IoTeM
Energy Management component collects the data through the gateway and MQTT driver,
analyzes, and displays information for all configured physical meters and virtual meters
located in the client premises. The analysis will primarily focus on parameters from the
HVAC section in building management system.
Thermal condition of a space will be classified as below, based on the variation in the
desired settings.
• Off
• Maintained
• Under Cooling
• Over Cooling
The platform evaluates various key performance parameters including:
• COP of the equipment calculated as the percentage of time is what an equipment can
maintain as the space temperature settings while its operating.
• Daily cooling Average.
• Temperature variation statistics.
• Identify cooling index, efficiency, condition of air side equipment's.
• Equipment and Space Ranking.
• Faulty sensor identification.
ML Outputs
• Auto silencing of alarms.
• Automated schedule optimization of air side equipment.
• Faulty readings and outliers.
SEAM – Smart Energy & Asset Management
Page 8
SEAM – Smart Energy & Asset Management
Equipment Stream
Data
Ambient Weather
Data Stream
Space Analytics,
Cooling Index
Processor and COP
evaluator
Space Analytics Suggestions:
1. Faculty sensor Identification
2. Space Optimization Metrics
Data Storage and
API engine
Space Operation
Mode Classifier
COP aggregator/
Validator
Space Analytics ML
Bridge (Platform
component)
COP and Cooling
Index Preprocessor
Data Preparation
Engine
Data Splitting
Training Data
Testing Data
Supervised
Learning
Final Model For
Prediction
LSTM Regression
Continuos RMSE
Validation
Stable Trained
Model
Tested Model
Validation metrics
to Data engine
c
Platform Suggestion:
1. Auto Schedule Suggestions based COP and
Cooling index Predictions
2. Temperature Alarm Configuration
Modification Recommendations
SEAM – Smart Energy & Asset Management
Page 9
SEAM – Smart Energy & Asset Management
USE CASE OF ENERGY ANALYSIS BY IOTEM
All building management systems were commissioned to control and monitor mainly the
HVAC assets to cater to the space requirements and meet the comfort levels of the
occupants. This was achieved by configuring the controllers in a predefined sequence of
operation procedures.
However, the system working as standalone controllers or equipment’s lacked the
intelligence to provide outputs based on previous history though the data was available
within the systems itself.
CHW consumption was a major contributor to the overall OPEX of any organization here in
the middle east. The overall operational strategy currently uses a trial-and-error approach
and requires constant monitoring and modifications to ensure the costs consumptions in
line with the defined OPEX KPI’s.
The AI module created for energy management was applied in this scenario to help manage
the consumption efficiently. The platform would help operations to forecast energy
consumption on buildings, verify them on standard benchmarks and help to plan on
measures required.

The analysis also helps in identifying peak consumption violations to trigger events
and engage dynamic FDD to identify the root cause.

The consumption/demand forecasting feature in the platform would apply to BTU
Meters.

Hourly consumption trends for various meters registered with the platform will be
utilized to train the algorithm.
The platform used Tensor flow on python and the training method was LSTM multivariate
regression to analyse the below independent variables.

Outside air temperature

Total conditioned area (for BTU)

Occupancy in the building (Currently not captured)

Type of the building (Commercial, Residential etc.)

Working hours (in case of residential, entire day
SEAM – Smart Energy & Asset Management
Page 10
SEAM – Smart Energy & Asset Management
Outcome was to predict Energy consumption. The platform then measures forecasted
consumption against the actual consumption recorded and if the variance exceeds 8%, will
automatically trigger retraining with fresh data samples from the platform.
Getting information one the forecasted energy allowed the facility management to carry out
operational strategies such as,
1. Proactive maintenance schedules to reduce consumption.
2. Auto scheduling of HVAC units to meet demand and avoid unwanted energy
losses.
3. Take informed decisions for shutdown activities of the Chilled water system.
4. Maintain the delta T of the building to avoid penalties from the district
cooling provider.
The cost saving is estimated to be between 10 to 15% according to the actions
implemented based on the recommendations from the platform outputs.
MARKET SIZE
Since 2015, global energy intensity has been declining continuously, which is gradually
increasing demand for energy management systems to save energy. Control over excessive
energy consumption and efficient management of energy resources are the key capabilities
that are accelerating the adoption of energy management solutions across the globe.
Market research analysts have confirmed that building energy management services in the
Middle East will grow steadily during the next four years and post a CAGR of more than 19%
by 2023. This market research analysis identifies the increasing need to optimize energy
consumption as one of the primary factors that will have a positive impact on the growth of
the energy management services market in the next few years.
SEAM – Smart Energy & Asset Management
Page 11
SEAM – Smart Energy & Asset Management
Owing to rapid infrastructure development activities, the Middle East witnesses a considerable
increase in energy consumption. It has been observed that energy consumption in the Middle East
was about 770 million tons of oil equivalent (MTOE) during 2015 and is only expected to move up at
a rate of over 4% YoY. This will encourage governments and enterprises in this region to optimize
their energy usage.
BARRIERS TO ENTRY
Nowadays, buildings are equipped with immense number of Internet of Things (IoT) and
smart devices. These devices are producing a vast amount of live data about the building.
There is no doubt that integrating the information can provide value; nonetheless, there are
some barriers and considerations when combining the building information with the live
data. This prevents many industries like the facility management industry to fully benefit
from this integration.
One of the reasons behind the emergence of this issue is the incomplete handover of the
building information through the building project’s life cycle, from the design to the
construction and occupancy.
As an example, many changes arise during the construction phase which should be
incorporated with the ‘as design’ plans to provide update plans (as-built plans).
Nevertheless, for most buildings only the ‘as design’ plans are handed over to proceed
further. This happens because of the unwillingness to invest time and budget to update the
building projects plans.
A further example is related to the need to refurbish or reconfigure the building spaces and
the mechanical/electrical systems happening during the occupancy phase. These changes
are required to be incorporated with the existing building information as well.
SEAM – Smart Energy & Asset Management
Page 12
SEAM – Smart Energy & Asset Management
The above-mentioned problems negatively impact the efficiency of the decision-making
processes for the platform’s algorithms due to insufficient data.
THREATS & OPPORTUNITIES
Digitalization of energy management creates opportunities to improve operations and
increase flexibility in operations and control of facilities. Conservative estimates supported
by analysis of real-life cases suggest that digital optimization can boost profitability by 20 to
30 percent. Utilities can realize most of this potential by three means: smart meters and the
smart grid, digital productivity tools for employees, and automation of back-office
processes. A few of the major opportunities are as listed below.

Distributed energy resources enabled by big data-driven alignment of supply and
demand.

Data driven asset strategies including predictive and condition-based maintenance
increasing the asset life cycle.

Platform supports distributed energy resources and marketplaces.

Field work force with mobile access to maps, data, work management tools.
On average, 30% of the energy used in commercial buildings is wasted,
according to the Environmental Protection Agency.
HVAC accounts for 41% of total energy used by commercial buildings
across the globe.
Source: https://www.energymanagertoday.com/
Tangible Benefits
Savings on energy spend:




Visibility on portfolio energy usage.
Detailed energy spends information at all spaces
within the portfolio.
Energy anomaly detection using FDD.
Energy forecast and performance analysis.
If used to its full potential, a
building analytics and fault
detection platform can
provide useful analytics and
Fault Detection Diagnostics to
assist customer in running a
more efficient building.
Benefits from higher efficiency buildings
include:




Operating costs decrease for both new &
existing building projects.
Building value increase for new & existing
properties.
Return of investment improves for new &
existing building projects.
Occupancy increase expected for both
new construction & existing buildings.
One significant hurdle is the lack of standardization. Much of the data coming from sensors
is not standardized or integrated across platforms. To improve performance, automation
SEAM – Smart Energy & Asset Management
Page 13
SEAM – Smart Energy & Asset Management
systems need to embrace proven digital strategies between each other’s proprietary
application languages.
The biggest opportunities in this space comes from the lack of vendor agnostic service in
the market. In effect that translates to clients being forced to go with a single vendor to
avoid duplication of monitoring and management setup. Our offering to manage the IoT
setup irrespective of the vendor would be a unique offering giving the clients the freedom
to go with the best of breed IoT sub-systems without cost overruns from duplication.
Furthermore, companies have not, traditionally, been particularly good at sharing data. This
must change. For digitalization to deliver all its potential benefits, it must be better
integrated, with systems, equipment, and sensors from across the value chain sharing data
and learning from each other. Many sites still rely on spreadsheets, combined with human
expertise, for crucial decision support.
SWOT ANALYSIS
Strengths
Weaknesses
Opportunities
Threats
Product
Data driven smart
Initially limited
Possible
Established field
Offering
energy
AI module
partnerships
players
management.
offering
for hardware &
Vendor agnostic.
cloud
integration
Brand /
New to market
Marketing
Brand building
Takes time to
opportunities
match brand
due to scale of
impact of
commercial
established
real estate
competition
market in UAE
Staff/HR
Smaller team of
Small but
tech-industry
cohesive team
experts
responds to
field with
agility
Finance
Large growth
Large players in
opportunities
market who has
better funding
Operations /
Public cloud
Management usage, fast
SEAM – Smart Energy & Asset Management
HR to scale
Partner with
System
operations to
other market
effectiveness
Page 14
SEAM – Smart Energy & Asset Management
deployments,
meet
integrators to
depends on
reusable AI
immediate
meet the
design plans &
modules.
growth
immediate
shared data
opportunities
growth
opportunities
Market
Retrofit on
Energy eff. still
Geo expansion
Established wide-
existing infra.
pursued for
and ability to
solution
Visualize, capture
economic
develop more
providers.
processed data
gains.
AI modules for
on any platform.
Environmental
larger
aspect plays
integration.
2nd fiddle.
Key strengths available to improve weaknesses/ combating threats
Smaller operational space initially that utilizes our expertise and solutions means, we can
respond faster to customer queries and support requests. This can pave way for favourable
customer feedbacks and wider opportunities in the market.
Immediate goals/next steps
Our immediate next steps are:
1. Analyse the data types that we would collect from the sensors to have the process
in place for efficient data engineering that helps to build and test functionalities of
the AI module.
2. Come up with a framework for utilizing processed data into actionable insights,
maintenance tasks and reverse input into the IoT controllers for equipment control.
3. Identify immediate project opportunities.
4. Identify direct sourcing of IoT components.
Our immediate goals are:
1. Concentrate on successful project deliveries that can use our IoTeM platforms.
2. Build and train a field integration and customer training team, as implemented
feature utilization and value identification is as important as gaining a project.
3. Partner with FM companies to spread our value proposition to large customers.
4. Identify local partners with whom we can work with or outsource our IoTeM tech to
integrate into their projects.
SEAM – Smart Energy & Asset Management
Page 15
SEAM – Smart Energy & Asset Management
Long-term goals/next steps
On a longer term we need to do the following:
1. Expand solutions to integrate AI modules to wider applications in BMS. Such as
lighting/powerline management, generating space utilization reports, connected
security management, etc.
2. Efforts to build brand recognition.
3. Identify new geo markets with growth opportunities.
4. Identify possible partners from local integrators in those new markets.
5. Expand the core team with more industry experts and identify new product offerings
that can integrate with IoTeM.
COMPETITION
Competitive Analysis Worksheet
Factor
SEAM
JCI
Honeywell
Schneider
Importance to
Customer
Product
IoTeM
JEM
Brand Value
W
S
S
S
2
Price
S
W
W
W
3
Quality
S
S
S
S
3
Service
S
S
S
S
2
Reliability
S
S
S
S
3
SEAM – Smart Energy & Asset Management
BPS
BA-Building
Analytics
Page 16
SEAM – Smart Energy & Asset Management
Location
W
S
S
S
(UAE)
(Global)
(Global)
(Global)
S
W
W
W
4
Scalability
S
S
S
S
2
Infra-
W
S
S
S
3
S
W
W
W
2
Product
3
Customization
Requirements
Vendor
Agnostic
TARGET CUSTOMER
Integration and deployment services allow experts to integrate IoT devices with IoT solutions
and deploy it into the existing/desired IT infrastructure. These services enhance business
agility and process efficiency. Property management companies, Owners’ associations, and
developers are focused on sustainability and energy savings as per the latest market
approach. This platform fits perfectly to their needs heling them achieve their business goals.
Furthermore, during the pandemic outbreak, the IoT and smart systems has helped to
maintain and remotely manage the assets in a community with limited resources on site for
many facility management service providers.
SEAM – Smart Energy & Asset Management
Page 17
SEAM – Smart Energy & Asset Management
REQUIREMENTS - DATA & PEOPLE
SEAM – Smart Energy & Asset Management
Page 18
SEAM – Smart Energy & Asset Management
Team structure and representative profiles
ROLE
Qty.
Nos
Project Manager
1
Business Analyst
1
Solution
Architect
1
Interface
Engineer
Technician/Asst
Technicians
3
6
Energy Auditor/
Certified Energy
Manager
1
Data Scientist
2
Programming
expert
4
PROFILE
Experienced in managing multi-technology integration projects, with
good understanding of Energy Management solutions and
commercial real estate domain.
Experienced Building Management System (BMS) power user - who
would understand the data flow definitions and build specification,
trigger/alarm cases and metadata/parameter list
Experienced data integration specialist - who has worked on
PLC/BMS/SCADA data interchange projects and would be able to
define and execute integration test cases
Hands-on expert in integrating and configuring diverse BMS controllers
and use data extraction/validation tools.
Should have expertise in installing control panels and DDC controllers.
Expertise in Quantify energy consumption to establish baselines for
energy use or need. Collect and analyse field data related to energy
usage. Compare existing energy consumption levels to normative data.
Identifying potential energy conservation measures related to HVAC.
Experienced data management specialist adept in configuring
metadata,
carry out data pre-processing and checking accuracy of models and
implementation based on domain knowledge
Hand on expertise in GUI interface, development of application
software such as Python, R, Tensor flow, visualization, and data base
management applications etc.
PRODUCT DEVELOPMENT ROADMAP
SEAM – Smart Energy & Asset Management
Page 19
SEAM – Smart Energy & Asset Management
IoTeM platform development involves building of 3 layers of functionality. These layers will
be built and enhanced across 4 phases of development.
Platform Architecture layers.
1. Application Framework for cloud-based server infrastructure.
2. User interaction and frontend graphics and dashboards
3. Field data flow/collection and gateway layer.
Layer 1 involves the setting up infrastructure of server and development of the applications
to carry out data analysis. APIs are also designed to interface with third party applications to
receive or push data for analytics. The server architecture is segregated as below to carry out
different functions in standalone mode and in interaction with other applications as well.
Layer 2 is the user interaction modules, such as graphical representation of assets, floorplans,
navigation across communities, dashboards reflecting energy status, heatmaps, widgets for
report generation etc. This layer is focused on client interests and is very essential to reflect
their requirements. This layer requires progressive development and must be customizable.
Layer 3 is the data collector and requires on field installation. The gateways can be purchased
from third party OEMs, however the drivers necessary to on board the data to the platform
needs to be prepared. This driver will help in receiving data inputs from field controllers using
the gateways and to give commands to the field devices to achieve ML related outputs.
These three layers will be developed and enhanced across different version releases of the
platform. The first 3 version releases are planned such that every version release enhances
the platform performance and adds on features to allow easier navigation and accessibility
to the platform modules.
Version-I: Initial release
The platform will be designed is to connect to the BMS (building management system) over
any available protocol such as BACnet/IP, OPC, Lon network etc. which forms the layer 3 of
the platform. Once data is collected, the platform will analyse the data using preconfigured
conditions/algorithms which is built in the layer 1. Based on the analysis notifications for
abnormalities are generated. The trained data model helps to provide outputs from the
platform back to the BMS onsite to control the assets as per the desired sequence of
operation. These SOPs ensures the assets give optimum outputs while consuming lesser
energy.
SEAM – Smart Energy & Asset Management
Page 20
SEAM – Smart Energy & Asset Management
The key features incorporated in version I release are:

Deployable on private environment

Utilizes unstructured database schematics.

Ability to create dashboards and benchmark various energy performance KPI.

Customized UI and platform graphics to cater needs of individual clients.

Fault Detection and Diagnostics

User Management

IoT Integration & Data Management

Monitor, report, and analyse using preconfigured rule engine.

Integrate and collect data from the BMS.

Generation of advisory reports based on comparison of pre-set sequence of
operation of the monitored assets with the actual response on site.
The first version will be ready for implementation in 6 months.
Version-II: Platform upgrade with Machine learning and AI features
After release of Version the platform is debugged and stabilized with the basic features for
a smart building model and the output commands are cross verified. During this period, the
database has collected enough data to start the implementation of prediction models or
machine learning analytics.
The enhancements in the platform will include.
 Asset Performance analytics functionality
 ML framework enhancement
 BI functional APIs to receive data from third party platforms/applications. Also, to
push data to the third-party applications.
 Different asset comparisons to provide asset efficiency dashboard and reports.
 Machine learning libraries for different domains.
The prediction models are data-driven models to forecast the energy behaviour of a
building according to some specific indicators. The prediction models are automatically
estimated and customized per building given the measure to be forecasted and the data
available.
Initial energy related output will be based on the rate of change in the space temperature
factoring the outside air temperature. We will measure the rate of change in space
temperature when the air conditioning is on and when off. This would let us understand
the mean duration for considerable change in temperature. We would use this to predict
the time required to reach the desired settings of the space. This information will be used
for optimized scheduling and energy management.
SEAM – Smart Energy & Asset Management
Page 21
SEAM – Smart Energy & Asset Management
The time duration for development, training of model, testing and retraining for accuracy
and implementation of algorithms on platform will be approximately 4 months from data
collection phase.
Version-III: Platform Enhancements
This version release is focused on the scalability of the platform to include more algorithms
based on the domain inputs provided. In this version we focus to increase the intelligence
of the platform to provide unified facilities and sustainability management software suite
that uses IoT to optimize building performance in real-time, across the portfolio. Some of
the machine learning algorithms that will be developed are,

Cause and effect on ventilation system based on weather prediction.

Faulty sensor/device identification.

Chiller, heat exchanger COP prediction and advisory report.

Robotic process automation in work order approvals.
Future Road Map
The features in the platform will be enhanced to allow facility management companies and
property management teams to carry out operations with much lesser resources and higher
accuracy. IoTeM will include modules that will help in

Making legacy systems smarter by integrating existing systems like BMS, BAS, Chiller
& HVAC, Fire, Security and Power Systems with everyday facility workflows using IoT

Improving asset health/performance with real-time Fault Detection and Diagnostics
and condition-based monitoring

Getting real-time actionable insights using asset performance and energy analytics
on contextual asset and energy data.

Enhanced visualization engine with user friendly navigation

Automated equipment operation sequence and schedules based on current weather
and previous equipment history.

Building digital twins for calibration of assets and increase asset life cycle.
Useful algorithms to build this product
Primary algorithms used for our ML functionalities are:
1.
Multivariate Linear regression
2.
Multivariate Linear regression with LSTM
3.
Deep learning for energy forecasting and trend analysis
SEAM – Smart Energy & Asset Management
Page 22
SEAM – Smart Energy & Asset Management
4.
Timeseries analytics
5.
Along with this we would derive custom algorithm with combination of
above-mentioned algorithms and strategies
6.
Phase 1 implementation would use supervised learning techniques
Training of Employees
The platform will provide visualization using notification center where findings of the
algorithms, suggestions from the process and automated actions taken by the platform for
achieving different targets would be presented for easy identification. This would ensure
that a minimum training would be required. The platform would also provide real-time
notification for anomaly detection, target violation etc. on the visualization layer.
EXPANSION PLAN
The data set used for learning process would be widened and expanded to different
portfolio of buildings classified on their type, operation policies, occupancies, and peak
time. This would ensure wide range of complex data and context would be provided to the
ML engine. Newer ML use cases, control strategies and target definitions can be created
which would demand our current ML engine to evolve into a robust ML and AI engine.
Since we are focusing on a customizable front-end platform, we will be able to implement
the solution for a wide portfolio other than energy management such as

Asset management

Remote analysis of construction machinery
SEAM – Smart Energy & Asset Management
Page 23
SEAM – Smart Energy & Asset Management

Fleet management using VMS (Vehicle monitoring system)

Utilities and infrastructure remote management such as treatment plants.
BUSINESS PLAN (FINANCIALS)
IoTeM as a platform is more focused as a service delivery rather than a product supply.
Since the application is focused on energy saving, we will move forward and open our
platform to individual owners as well as facility management or property management
services, which will allow for instant cash flow and expand the company portfolio.
Almost all the costs are recurring, or operational related, onetime costs are only the
gateway installation which will be converted to a service-based pricing to include the
maintenance contract and recover the initial capital.
The detailed cost and financials are as explained below.
One Time Costs
Cost Item
Amount
Registration
Office Setup (Capex)
Workstations/Servers
and Logistics
Branding
Certifications
$ 7000-8000
$ 4000
$ 30,000
$ 20,000
$ 10,000
EXPENSES
SEAM – Smart Energy & Asset Management
Page 24
SEAM – Smart Energy & Asset Management
Below detailed are the expenses considered for the platform development, operation, and
maintenance requirements.
Cost
Summary
Y1
Y2
Y3
Y4
Y5
Total
Dev & Run Costs
Cloud hosting
Platform Cost
Development
Costs
SG&A
Rent
Employee
salaries
Operational &
Maintenance
costs
Subtotal Costs
$55,330.55
$83,000.00
$110,669.00
$110,669.00
$110,669.00
$470,337.55
$150,000.00
$83,000.00
$50,000.00
$50,000.00
$50,000.00
$383,000.00
$100,000.00
$100,000.00
$100,000.00
$150,000.00
$150,000.00
$600,000.00
$25,000.00
$25,000.00
$25,000.00
$30,000.00
$30,000.00
$135,000.00
$518,478.26
$642,391.30
$815,217.39
$815,217.39
$854,347.83
$3,645,652.17
$50,000.00
$50,000.00
$100,000.00
$100,000.00
$100,000.00
$400,000.00
$1,200,886.39
$1,255,886.39
$1,295,016.83
$898,808.81
$983,391.30
$5,633,989.72
REVENUE
SEAM – Smart Energy & Asset Management
Page 25
SEAM – Smart Energy & Asset Management
The platform will be offered as a SaaS and will charge on subscription. The platform will be
available for subscription in three tiers.



Elite
Advanced
Basic
The platform will be charged based on the number of data points collected, tier of servicing
and support level agreements. The solution would also be offered as premium enterprise
offering to larger customers.
Revenue Projections
The Projections are made based on the number of points per contract. The contract value is
distributed against the 3 different categories that will be signed each year for the next 5
years.
Revenue Summary
Y1
Y2
Y3
Y4
Y5
Total
Revenue
$Elite
-
$1,300,000.00
$1,300,000.00
$1,300,000.00
$1,950,000.00
$5,850,000.00
Advanced
-
$800,000.00
$800,000.00
$1,600,000.00
$2,000,000.00
$5,200,000.00
$300,000.0
0
$300,000.
00
$600,000.00
$1,200,000.00
$1,800,000.00
$2,400,000.00
$6,300,000.00
$2,700,000.00
$3,300,000.00
$4,700,000.0
0
$6,350,000.0
0
$17,350,000.00
$3,000,000.00
$6,300,000.00
$11,000,000.
00
$17,350,000.
00
$17,350,000.00
Basic
Total
Revenue
View
Cumulativ
e Revenue
$300,000.
00
ROI
SEAM – Smart Energy & Asset Management
Page 26
SEAM – Smart Energy & Asset Management
Cost Summary
Dev & Run Costs
Y1
Cloud hosting
Platform Cost
Development Costs
SG&A
$55,330.55
$150,000.0
0
$100,000.0
0
Rent
Employee salaries
$25,000.00
$518,478.2
6
Operational &
Maintenance costs
Subtotal Costs
Cost Item
Registration
Office Setup (Capex)
Workstations/Server
s and Logistics
Branding
Certifications
Subtotal Costs
Total Cost view
Cumulative Costs
$50,000.00
$898,808.
8
Total Revenue
View
Cumulative
Revenue
Net cash flow
RoI
Y3
Y4
Y5
Total
$83,000.00
$110,669.00
$110,669.00
$110,669.00
$470,337.55
$83,000.00
$50,000.00
$50,000.00
$50,000.00
$383,000.00
$100,000.00
$100,000.00
$150,000.00
$150,000.00
$600,000.00
$25,000.00
$25,000.00
$30,000.00
$30,000.00
$135,000.00
$642,391.30
$815,217.39
$815,217.39
$854,347.83
$3,645,652.17
$50,000.00
$100,000.00
$100,000.00
$100,000.00
$400,000.00
$983,391.30
$1,200,886.3
$1,295,016.83
$5,633,989.72
$1,255,886.3
9
Amount
$8,000.00
$4,000.00
-
-
-
-
-
$30,000.00
-
-
-
-
-
$20,000.00
$10,000.00
-
-
-
-
-
$72,000.0
0
$970,808.
8
$970,808.
8
Revenue
Summary
Revenue
Elite
Advanced
Basic
Y2
$0.00
$0.00
$983,391.30
$1,200,886.3
$1,954,200.1
$3,155,086.5
Y1
Y2
$0.00
$1,255,886.3
9
$4,410,972.9
0
Y3
Y4
$0.00
$72,000.00
$1,295,016.83
$5,705,989.72
$5,705,989.72
$5,705,989.72
Y5
Total
$-
-
$1,300,000.00
$1,300,000.00
$1,300,000.00
$1,950,000.00
$ 5,850,000.00
-
$800,000.00
$800,000.00
$1,600,000.00
$2,000,000.00
$ 5,200,000.00
$600,000.00
$1,200,000.00
$1,800,000.00
$2,400,000.00
$ 6,300,000.00
$300,000.0
0
$300,000.00
$2,700,000.00
$300,000.00
$3,000,000.00
$670,808.81
8
-69%
$3,300,000.00
$6,300,000.0
$1,045,799.8
SEAM – Smart Energy & Asset Management
$6,350,000.00
$ 17,350,000.00
$11,000,000.0
$17,350,000.0
$ 17,350,000.00
$3,144,913.4
9
54%
$4,700,000.00
$6,589,027.1
0
100%
$11,644,010.2
8
149%
$11,644,010.2
8
204%
204%
Page 27
RISKS