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Developing data-driven models to predict BEMS energy consumption for demand response systems

From National Research Council Canada

DOIResolve DOI: https://doi.org/10.1007/978-3-319-07455-9_20
AuthorSearch for: 1; Search for: 1; Search for: 1
Affiliation
  1. National Research Council of Canada. Information and Communication Technologies
FormatText, Book Chapter
Conference27th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE 2014), June 3-6, 2014, Kaohsiung, Taiwan
Subjectcooling systems; electric load forecasting; intelligent systems; learning algorithms; learning systems; models; office buildings; weather forecasting; air handling units; building energy management systems; data-driven model; demand response; energy consumption prediction; energy-saving measures; forecast information; residential building; energy utilization
Abstract
Energy consumption prediction for building energy management systems (BEMS) is one of the key factors in the success of energy saving measures in modern building operation, either residential buildings or commercial buildings. It provides a foundation for building owners to optimize not only the energy usage but also the operation to respond to the demand signals from smart grid. However, modeling energy consumption in traditional physic-modeling techniques remains a challenge. To address this issue, we present a data-mining-based methodology, as an alternative, for developing data-driven models to predict energy consumption for BEMSs. Following the methodology, we developed data-driven models for predicting energy consumption for a chiller in BEMS by using historic building operation data and weather forecast information. The models were evaluated with unseen data. The experimental results demonstrated that the data-driven models can predict energy consumption for chiller with promising accuracy.
Publication date
PublisherSpringer International Publishing
In
  • Modern Advances in Applied Intelligence: 27th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2014, Kaohsiung, Taiwan, June 3-6, 2014, Proceedings, Part I (2014): 188–197.
Series
  • Lecture Notes in Computer Science, no. 8481.
LanguageEnglish
Peer reviewedYes
NPARC number21272679
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Record identifier84fae62b-fdd8-4e41-9ed3-09c71557c297
Record created2014-12-03
Record modified2020-06-18
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