Journal article
Data-driven building load profiling and energy management
Sustainable cities and society, Vol.49, p.101587
08/2019
DOI: 10.1016/j.scs.2019.101587
Abstract
•A systematic process of using smart metering data to quantify building daily load profiles.•Prediction models of load profiles are trained from historical energy consumption and environment data.•Anomaly energy consumption is detected by control chart technology.•Case studies are done by installing intelligent energy management system in real-world commercial buildings.
Commercial buildings consume a lot of energy and contribute a significant part of greenhouse gas emission. Many energy-saving or green-building initiatives were compromised by equipment and human-related faults under the umbrella of poor facility management. Data-driven building energy management is a cost-effective approach to improve energy efficiency of commercial buildings, and gains more and more popularity worldwide with the deployment of smart metering systems. This paper developed a systematic process of using smart metering data to quantify building daily load profiles (i.e. energy consumption patterns) with a set of statistics, e.g. base load, peak load, rising time and so on. Then prediction models of these building load statistics are constructed from historical training data consisting of energy consumption, environment and holiday information. At last residuals of the prediction models are analyzed to form statistical control charts. As a result anomaly energy consumption could be detected by comparing the predicted statistics and observed ones, which will help building managers to locate problems just in time. The effectiveness of the proposed solution is verified through real-world data analysis and computational studies.
Details
- Title: Subtitle
- Data-driven building load profiling and energy management
- Creators
- Jin Zhu - Nanjing UniversityYingjun Shen - Nanjing UniversityZhe Song - Nanjing UniversityDequn Zhou - Nanjing UniversityZijun Zhang - University of Hong KongAndrew Kusiak - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Sustainable cities and society, Vol.49, p.101587
- Publisher
- Elsevier Ltd
- DOI
- 10.1016/j.scs.2019.101587
- ISSN
- 2210-6707
- eISSN
- 2210-6715
- Grant note
- DOI: 10.13039/501100001809, name: National Natural Science Foundation of China, award: 71834003, 71573121, 71001050; name: Research Grants Council, University Grants Committee, Hong Kong, award: 11272216
- Language
- English
- Date published
- 08/2019
- Academic Unit
- Nursing; Industrial and Systems Engineering
- Record Identifier
- 9984187079502771
Metrics
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