Ammar Ahmed, Karsten Menzel, Joern Ploennigs, Brian Cahill
IRUSE, University College Cork, Ireland k.menzel@ucc.ie
Abstract : Current buildings‟ performance assessment tools are deficient in their ability to integrate and process building monitoring data to generate actionable information that can assist in achieving a higher level of building performance. Therefore, this paper addresses this problem by proposing the design and implementation of a multi-dimensional data analysis concept for building monitoring data. Firstly, we describe the development of the multi-dimensional model .
This model is implemented using data warehouse technology. Secondly, we emphasize on the use of dimensional data for the categorization of sensed and metered fact data. Finally, we describe the need for data aggregation supporting complex analysis tasks of different stake holders.
1 Introduction
Several authors estimate that energy savings of 15-40% could be made in commercial buildings by closely monitoring and supervising energy-usage and related data (Salsbury &
Diamond 2000). Furthermore, it is argued that the collection and analysis of sensed and metered building performance data has allowed for the discovery of previously unknown building performance characteristics, relationships, dependencies, or trends (Rob et al. 2008, p. 744). It is also argued that collecting building performance data and the analysis process should be pursued in tandem (Hardy & Bryman 2004, pp. 9-10) since both are important prerequisites for the development of intelligent buildings (IB) operating in a user friendly and energy-efficient way.
A holistic definition of IB is given by Caffery (1985): „ as one that provides a productive and a cost-effective environment through optimization of its four basic elements: structure, systems, services, and management and the interrelationships between them
‟. From a computer science point of view an IB utilizes advanced IT-systems to monitor the building so as to optimize user comfort, energy consumption, and further operational and maintenance processes. IBs monitor building performance to learn from sensed and metered data (Kasabov,
1998) and to generate actionable information (Sharples et al. 1999).
The usage of wireless sensors and meters to monitor and control building services and energy consumption is fast advancing. This leads to an increase in available (bulk) data. However, current systems are insufficiently integrated with data management and analysis tools. Their capabilities to contribute to an improved level of building performance are limited. Therefore, the development of an integrated information model is required, allowing the holistic, multidimensional management of building performance data in relation to the buildings energy usage (O'Sullivan et al. 2004). This would support the aggregation and analysis of building performance data and ease the extraction of further information to support decision processes for building control and operation. Improved decision support systems (DSS) could be provided as an arrangement of computerized tools (Rob et al. 2008, p. 705) that will help the decision making and ensure flexible, adaptable, and profitable operation of a building over its life span (Hagras et al. 2003).
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2 About Data Warehouse Technology
This paper describes research results developed in University College Cork (UCC). The authors propose to develop a so called Energy Building Information Model (eBIM) integrating a Building Management System (BMS) with wireless sensing and metering tools, and standardized building product and process models (e.g. modeled and implemented in IFC).
The objective of this paper is to demonstrate how to integrate and analyze data from different data sources in buildings .
This involves maintaining historical building performance data, analyzing user comfort and energy consumption as two major indicators of building performance. It also involves the design of data aggregation algorithms, and the development of a DSS for the planning and maintenance of facility management processes. This paper presents a multi-dimensional data analysis concept for building performance data. A data warehouse (DW) was designed and implemented, which utilizes and integrates data from different data sources in a building to generate reports, actionable information , and data aggregations (see Figure 1).
Different Data Sources in a Building
Information Model
Building Information Model
Cost & Financial Information Model
Wireless Sensors Network
-Data Acquisition Model-
Wired Building Information Management System
-Control Model-
Data Integration
Data Warehouse
Actionable Information
& Reports
Figure 1: Data Sources, Information Model, and Data Warehouse
Currently, the main problem in Energy and Facility Management is the unavailability of consistent and complete building performance data. Traditional database management systems
(DBMS) are used to store building monitoring data. These DBMS lack the ability to create data aggregations and do not support the analysis of building performance data to deliver reports and actionable information (Lane 2007, p. 29). Information requirements become complex, as it is difficult for a DSS to extract information from the data found in buildings‟ databases. Therefore, DW technology is introduced to manage and analyze building monitoring data in an integrated way (Rob et al. 2008, p. 703) .
A DW is designed to support data analysis. It contains historical data derived from transaction data. It separates the analysis workload from the transaction workload. This helps to maintain historical records and achieve better understanding of the business processes (Lane 2007, p 29). A DW environment includes an extraction , transformation, and loading component
(ETL), an online analytical processing engine (OLAP), and client analysis tools. Inmon
(1996) described DW as „ a subject oriented, integrated, non-volatile, and time variant collection of data in support of management’s decisions ‟
DW technology has been used in business and marketing to improve business processes
(Rainardi 2007, pp. 441-446). It supports complex queries and more sophisticated features for aggregating, analyzing, and comparing data (Tjoa 2005, p. 1). It is also used to discover trends and patterns in data (Han & Kamber 2001, p. 455) and to finally prove the strength of truth of hypotheses (Inmon 2005, p. 382).
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2.1 DW Technology for Construction and Building Performance Management
DW technology has been introduced to the construction management domain to improve the management of historical data (Ahmed & Nunoo 1999), (Lee & Lee 2002).
Publications report about the development of a module designed to help the construction manager during the construction process (Chau et al. 2003) or to support the selection of the most appropriate site for development (Ahmed et al. 2004). DW technology has been also applied to support the exchange of documents amongst multiple parties in construction projects (Zhiliang et al. 2005). However, few efforts were made to explore the impacts of DW technology on building performance management supporting the creation of sophisticated data aggregation and analysis tools. Building performance analysis requires a set of measurable building characteristics specifying the primary goal of buildings to serve user needs (CIB 1985). Building performance analysis emphasizes to measure and assess these performance characteristics (Hartkopf et al. 1986, pp. 5-22). The evaluation of building performance is the comparison of the actual building performance and the expected criteria of its performance (Wolfgang 2005, p. 7).
Therefore, this paper initially introduces the usage of Data Warehouse technology to
Buildings Performance Data Analysis (BPDA). The next section describes building data sources . This is followed by an explanation of how the building performance data is managed.
It details information modeling concepts and their application to BPDA. An overview of the multi-dimensional data analysis concept is post ceded by finally detailing the approach for data aggregation and multi-dimensional data analysis.
3 Building the Data Warehouse for Building Performance Management
The DW architecture described in this paper integrates and analyzes data from the wired
BMS, an ad-hoc wireless sensor network, and building information model (BIM). It maintains the historical building performance data of UCC‟s Environmental Research Institute (ERI
2002) building. The ERI building is used by multiple research groups as a “Living
Laboratory” to demonstrate the IB concepts. The next chapters illustrate the design and implementation process of a Data Warehouse system to analyze user comfort and energy consumption. All logical definitions and their physical implementation presented in this paper comply to Oracle Corporation Specifications for Oracle DW-Builder 11g version 1 (11.1)
(Oracle 2008).
In the following chapters we use the generation of reports for monthly energy consumption for different stakeholders as an example of the application of DW-technology. To generate these aggregated reports we need to integrate data from different data sources, which include
BMS and BIM.
A BMS is a computer based system to integrate monitoring and control building‟s services.
The collected data is usually in different format as it uses wireless and wired sensors, meters and actuators. Sensed and metered performance data is regarded as fact data (Rob et al. 2008, pp. 5-7). This data needs to be extracted from the BMS files, transformed (Sapsford & Jupp
2006, p. 161) and finally loaded into the DW. To structure fact data, so called dimensional data is required. Dimensional data is descriptive data which could be extracted from the BIM, which is a holistic and integrated description of building specifications (Zarli 2008, p. 467).
The existing BIM-systems use different data storage systems. Product and process model servers might provide database interfaces allowing the export of so called external tables.
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Other systems might consist of a collection of physical files. All source data is compiled and pre-processed in a staging area. The staging area is where all source data is cleansed and consolidated, before being loaded into the DW-core (see Figure 2).
Source Files
Extraction
& Mapping
Staging Area
Loading Data in DW
External Tables
Bulk Data Modification Techniques
Facts Data
Dimensional
Data
Mapping
Cubes
Output Aggregations
Query Profiles
Figure 2: Processing of Building Performance Data
Following the scenario illustrated in figure 2, to analyze the building performance data (e.g. the building‟s monthly energy consumption) we need to define dimensions and import dimensional data. For example, the dimension zone has the building as one of its levels or the dimension times has the month as one of its levels. The descriptive information, also called dimensional data , is mapped from the external tables of the BIM to the corresponding target dimensional tables (Kimball et al. 2007, pp. 240-241).
External Tables are relational objects to access data from another data source (Richard 2007, p. 381) and used to populate the building data dimensions and cubes. Mapping is a technique to move data between DW components.
Furthermore, fact data is extracted from the BMS output files (Rob et al. 2008, pp. 5-7) and stored in a staging area. These files are loaded into the DW as source files and mapped to tables in the staging area. Finally the measures (facts) are mapped to a designated target cube that is designed to solve a specific business problem (Kimball & Caserta 2004, p. 248), such as the building‟s monthly energy consumption.
3.1 The Data Warehouse Schema
To provide query efficiency to the DW we use a de-normalized multi-dimensional star schema (see figure 3). The star schema is a data modeling technique to ease implementation.
Mesdevs_Dim
Device_Id (PK)
Device_Name
Device_Type
Manufacture
……..more attributes…….
Zones_Dim
Zone_id (PK)
Room_name
Building_location
……..more attributes….
Measurement_Stream
Measurement_Id (PK)
Organization_Dim_Id (FK)
Zones_Dim_Id (FK)
Mesdevs_Dim_Id (FK)
Times_Dim_Id (FK)
Energy_Measures
Temperature_Meausre
……..More business
…..Attributes……..
Times_Dim
Times_id (PK)
Start_Day
Cal_Month_Name
……..More times …….
……Attributes… …….
Organization_Dim
Organization_id (PK)
Group_Name
Department_Role
……..More Attributes……….
Figure 3: The Building‟s De-Normalized Multi-Dimensional Star Schema
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The basic star schema has four components: facts, dimensions, attributes, and attribute‟s hierarchies. Fact data is associated with numeric measures.
Dimensional data categorize the facts ( Pedersen & Jensen 2001).
A Dimension is organized into a hierarchy composed of numerous levels, each representing details required for the desired analysis. Dimensions provide further context information to the facts. Dimension s are used to aggregate data at the desired level.
In our example we organized the building descriptive data in multiple dimensions to describe the measured data from the BMS (Tjoa & Trujillo 2005, p. 1), such as (1) Zone Dimension,
(2) Time Dimension, and (3) Organisation Dimension.
Given our example considering the generation of a report about the monthly energy consumption of individual stakeholders, we must ensure that the energy consumption of a building can be documented on zone-level. Therefore, a zone is defined as the smallest space that can be sensed. A zone dimension can be organized in one hierachy composed of a
(a) zone level, (b) room level, (c) floor level, and (d) the building level. Each of these levels might be required by the desired analysis of building performance. Using this approach will facilitate aggregating buidling performance data at any level e.g. energy consumption can be aggregated for a room at the room level or for a building, at the building level.
Generalizing our example, a DW is designed as a multi-dimensional model . Individual dimensions are implemented as target dimensional objects. In a relational implementation a level attribute of a dimension becomes a column in the dimension table (Oracle 2008, pp. 228-239). The target dimensional objects are the data structures of the building specifications. Data within these dimensions is subject to change, we use the slowly changing dimensions (Oracle 2008, pp. 242-247) technique to determine how to store the historical change in the dimension‟s values. Our multi-dimensional model defines four dimensions :
1.
Zones_dim: It structures the physical locations of a building. It aggregates data from the zone level to the room level, floor level, building level, site level, city level and to the country level. Figure 4 shows a single hierarchy with a geographical roll up and related attributes. A zone is the smallest space been sensed.
Zone
Att. Id, Name
Room
Att. Id, Name
Floor
Att. Id, Name
Building
Att.: Id, Name
Site
Att.: Id, Name
City
Att. Id, Name
County
Att. Name
Figure 4: Roll Up for the Zones Dimension and related Attributes
2.
Organizaztion_dim: Is a structure reflecting the managerial aspects of a facility. It aggregates data from the end user or from an individual level of a zone to the group level, department level, branch level, and to the organization level, in a single hierarchy.
3.
Times_dim: Is the structure of time of how we would like to look at the building‟s historical records. This structure aggregates data from the second level to the minute level, hours level, day level, month level, quarter level and to the year level, in a single standard calendar year hierarchy.
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4.
Mesdevs_dim: Is a structure for all the specifications of the measuring devices that are used to monitor and control the building, such as sensors and meters. It contains a single hierarchy with a categorical roll up. It aggregates data from the devices level to its subcategories level to its category level.
3.2 Aggregation of Bulk Performance Data
A general concept of DW is to add redundancy to the data schema to support the efficient processing of complex retrieval patterns (Hoberman 2007). The (intermediate) results of frequently used, complex retrieval patterns are pre-calculated. Intermediate results are often aggregated data. Therefore, data is divided into measures and contexts describing these measures, as to build a dimensional model (Kimball et al. 2007, pp. 233-235) to ease the aggregation of data. Aggregated data is stored in so called data cubes. A cube could be interpreted as a data view in a multi-dimensional model.
In the case of our example aggregated performance data can be generated from the available fact and dimensional data which includes sensed and metered data from the BMS; i.e. different stakeholders can retrieve customized information about the building performance history. The availability of aggregated data, such as energy consumption per zone ( room, floor, etc.), per user (owner, tenant, etc.), or per time period (week, year, etc.), is a very important prerequisite for optimized building management to oversee energy consumption, control operation and maintenance costs to provide a steady high level of user comfort at minimal energy consumption.
In the case of our example the cube monthly_energy_measures aggregates the monthly (the month level in the time dimension) energy consumption (facts, measures from the BMS) for each building (the building level in the zone dimension). The cube‟s dimensionality references two dimensions, the month level in the times_dim and the building level of the zone_dimension (see Figure 3). The DW target module used has been implemented in Oracle
11g engine using Oracle Warehouse Builder 11g as a development environment.
4 Data Sources and Expected Data Volume
This chapter illustrates our approach to build the target DW components which support building performance management using the scenario and definitions explained in section 2 above. As mentioned in chapter 3 the ERI building is used for this research.
The ERI building is a 4500 m² “Living Laboratory” located on the campus of UCC, Ireland.
The building has a wireless sensor network installed along with a wired BMS system. It is equipped with multiple types of solar panels, geothermal heat pumps and an under floor heating system. Building Performance Data is provided by 180 (wired) sensors. Additionally, a test bed for wireless sensors and actuators has been installed since April 2008 in three phases. Demonstrator 0 has been operational since June 2008. Table 1 shows the expected data volume for the ERI building per year. Sensors include humidity, temperature, CO2, and lighting sensors. Meters include devices to measure electricity, mains water, cold water, gas, lighting energy consumption, boiler heat, solar heat and under floor heat.
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Table 1: Expected Data Volume for the ERI
Devices
Wired Sensors
Wireless Sensors
Meters
Total Volume
Interval Reading/Day Readings/Year No. of Units Records/Year
15 min.
1 min. different
96
1440
4005
35,040
525,600
1,461,825
180
80
(65)
6,307,200
42,048,000
1,461,825
49,817,025
The rough data is usually provided as either comma separated values source files (Friedl
2006, p. 213), or as external (relational) tables. This rough data is restructured and mapped to
DW objects to either populate the target fact table(s) or the target dimensional tables. The fact table(s) is where we store all monitored building performance data (Meersman & Tari 2007).
Fact table(s) is related to dimensional tables usually using foreign keys (Kimball et al. 2007, pp. 240-241). The target dimensional cubes are generated by another mapping process of fact and dimensional data to finally populate the target dimensional cubes.
5 Conclusion
The multi-dimensional building model presented in this paper is a pre-requisite for developing an intelligent building concept, by combining data from different building data sources in a single repository. Implementing multi-dimensional modeling using data warehouse techniques facilitates the aggregation of information at all desired levels concerning the building performance aspects. The data warehouse concept described here will be used for the ITOBO
(2007) project to develop a decision support system for the facility management sector. A process of gathering more buildings‟ specifications to populate the dimensions, and meet all stake holders‟ requirements, is currently ongoing. Defining additional scenarios based on the data warehouse to address the building performance aspects such as building diagnostics, and maintenance scheduling, is also in development.
Work in the Strategic Research Cluster „ITOBO‟ is founded by a grant of Science Foundation
Ireland and additional contributions from 5 industry partners.
References
Ahmed, I., & Nunoo, C. (1999). Data Warehousing in the Construction Industry: Organizing and Processing
Data for Decision Making. 8DBMC (pp. 2395-2406). Vancouver: NRC Research Press.
Ahmed, I., Azhar, S., & Lukauskis, P. (2004). Development of a decision support system using data warehousing to assist builders/developers in site selection. Automation in Construction , 13 (4), 525-542.
Caffrey, R. (1985). The Intelligent Building, an ASHRAE opportunity. ASHRAE Technical Data Bulletin , 4 (1).
Chau, K., Cao, Y., Anson, M., & Zhang, J. (2002). Application of data warehouse and Decision Support System in construction management. Automation in Construction , 12 (2), 213-224.
CIB. (1985). Working with Performance in Building.
Centre International du Batiment: CIB Report Publication.
ERI. (2002). Environmental Research Institute. Cork, Ireland: University College Cork, http://eri.ucc.ie.
Friedl, J. (2006). Mastering regular expressions (3 ed.). O'Reilly.
7

Hagras, H., Callaghan, V., Colley, M., & Clarke, G. (2003). A Hierarchical Fuzzy-Genetiv Multi-Agent
Architecture for Intelligent Buildings Online Learing, Adaptation and Control. Informatics and Computer
Science , 150 (1-2), 33-57.
Han, J., & Kamber, M. (2001). Data Mining: Concpts and Techniques.
Morgan Kaufmann Publisher.
Hardy, M. A., & Bryman, A. (2004). Handbook of Data Analysis.
Sage Publications.
Hartkopf, V. H., Loftness, V. E., & Mill, P. D. (1986). The Concept of Total Building Performance and Building
Diagnostics. In G. Davis, Building Performance (pp. 5-22). Phladelphia: ASTM publication code number (PCN)
04-901000-10, ASTM International, ISBN 0803104588.
Hoberman, S. (2007). Data Modeling Made Simple: A Practical Guide for Business & IT Professionals.
Technics Publications.
Inmon, W. (1996). Building the Data Warehouse.
John Wiley & Sons.
Inmon, W. H. (2005). Building the Data Warehouse (4 ed.). John Wiley and Sons.
ITOB. (2007). Information & Communication Technology for Sustainable and Optimised Building Operation.
Cork, Ireland: http://zuse.ucc.ie/itobo/.
Kasabov, N. (1998). Introduction: Hybrid Intelligent System Adaptive Systems. International Journal of
Intelligent Systems 16 , 13 (6), 453-454.
Kimball, R., & Caserta, J. (2004). The Data Warehouse ETL Toolkit: Practical Techniques for Extracting,
Cleaning,Conforming, and Delivering Data.
John Wiley and Sons.
Kimball, R., Ross, M., Thornthwaite, W., & Becker, B. (2007). The Data Warehouse Tool Kit.
(2, Ed.) John
Willey & Sons.
Lane, P. (2007). Data Warehousing Guide, 119g Release 1 (11.1), Oracle Data Base, .
Oracle.
Lee, J., & Lee, H. (2002). Principles and Strategies for applying Data Warehouse Technology to Construction
Industry. ECPPM, eWork and eBusiness in Architecture, Engineering and Construction (pp. 341-353). Slovenia:
Swets & Zeitlinger.
Meersman, R., & Tari, Z. (2007). On the Move to Meaningful Internet Systems.
Springer.
Oracle. (2008). Oracle Warehouse Builder, User's Guide, 11g Release 1 (11.1).
Oracle.
O'Sullivan, D. T., Keane, M. M., Kelliher, D., & Hitchcock, R. J. (2004). Improving Building Operation by
Tracking Performance Metrics throughout the Building Lifecycle (BLC). Energy and Building , 36 (11), 1075-
1090.
Pedersen, B. T., & Jensen, S. (2001). Multi Dimensional Data Base Technology. Distributed Systems Online , 34
(12), 40-46.
Rainardi, V. (2007). Building a Data Warehouse: With Examples in SQL Server.
Springer.
Richard, J. (2007). Oracle Database 10g Performance Tuning Tips & Techniques.
McGraw-Hill Professional.
Rob, P., Coronely, C., & Crockett, K. (2008). Data Bases Systems: Design, Implementation and Management.
Cengage Learning EMEA.
Salsbury, T., & Diamond, R. (2000). Performance Validation and Energy Analysis of HVAC Systems using
Simulation. Energy and Building , 32 (1), 5-17.
Sapsford, R., & Jupp, V. (2006). Data collection and analysis (2 ed.). SAGE.
Sharples, S., Callaghan, V., & Clarke, G. (199). A Multi-Agent Architecture for Intelligent Building Sensing and
Control. Sensor Review , 19 (2), 135-140.
Tjoa, A. M., & Trujillo, J. (2005). Data Warehousing and Knowledge Discovery.
Copenhagen: Springer.
Wolfgang, F. (2005). Assessing building performance.
Butterworth-Heinemann.
Zarli, A. (2008). Ework and Ebusiness in Architecture, Engineering and Construction: Ecppm 2008.
CRC Press.
Zhiliang, M., Wong, K., Heng, L., & Jun, Y. (2005). Utilizing exchanged documents in construction projects for decision support based on data warehousing technique. Automation in Construction, Elsevier , 14 (3), 405-412.
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