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Optimizing Legacy Building Operation: The Evolution Into Data-Driven Predictive Cyber-Physical Systems
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. NEC Laboratories Europe, Heidelberg.
Research Institute of Energy and Environment of Heidelberg (ifeu), Germany.
NEC Laboratories Europe, Heidelberg, Germany.
Honeywell ACS Global Labs, Prague, Czech Republic.
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2017 (English)In: Energy and Buildings, ISSN 0378-7788, E-ISSN 1872-6178Article in journal (Refereed) Epub ahead of print
Abstract [en]

Fossil fuels serve a substantial fraction of global energy demand, and one major energy consumer is the global building stock. In this work, we propose a framework to guide practitioners intending to develop advanced predictive building control strategies. The framework provides the means to enhance legacy and modernized buildings regarding energy efficiency by integrating their available instrumentation into a data-driven predictive Cyber-Physical System. For this, the framework fuses two highly relevant approaches and embeds these into the building context: the generic Model-Based Design Methodology for Cyber-Physical Systems and the CRoss-Industry Standard Process for Data Mining. A Spanish school’s heating system serves to validate the approach. Two different data-driven approaches to prediction and optimization are used to demonstrate the methodological flexibility: (i) a combination of Bayesian Regularized Neural Networks with Genetic Algorithm based optimization, and (ii) a Reinforcement Learning based control logic using Fitted Q-Iteration are both successfully applied. Experiments lasting a total of 43 school days in winter 2015/2016 achieved positive effects on weather-normalized energy consumption and thermal comfort in day-to-day operation. A first experiment targeting comfort levels comparable to the reference period lowered consumption by one-third. Two additional experiments raised average indoor temperatures by 2K. The better of these two experiments only consumed 5% more energy than the reference period. The prolonged experimentation period demonstrates the Cyber-Physical System-based approach’s suitability for improving building stock energy efficiency by developing and deploying predictive control strategies within routine operation of typical legacy buildings.

Place, publisher, year, edition, pages
2017.
Keyword [en]
Energy Efficiency, Predictive Control, Cyber-Physical System, Reinforcement learning, Optimization, Neural Network, Evolutionary Algorithms
National Category
Computer Science
Research subject
Mobile and Pervasive Computing
Identifiers
URN: urn:nbn:se:ltu:diva-63299DOI: 10.1016/j.enbuild.2017.05.002OAI: oai:DiVA.org:ltu-63299DiVA: diva2:1094512
Funder
EU, FP7, Seventh Framework Programme, 288409
Available from: 2017-05-10 Created: 2017-05-10 Last updated: 2017-05-10

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Publisher's full texthttp://www.sciencedirect.com/science/article/pii/S0378778816319983

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CiteExportLink to record
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