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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 1872-6178, Vol. 148, p. 257-279Article in journal (Refereed) Published
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 2 K. 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
Elsevier, 2017. Vol. 148, p. 257-279
Keywords [en]
Energy Efficiency, Predictive Control, Cyber-Physical System, Reinforcement learning, Optimization, Neural Network, Evolutionary Algorithms
National Category
Computer Sciences Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
URN: urn:nbn:se:ltu:diva-63299DOI: 10.1016/j.enbuild.2017.05.002ISI: 000404705000021Scopus ID: 2-s2.0-85019391553OAI: oai:DiVA.org:ltu-63299DiVA, id: diva2:1094512
Funder
EU, FP7, Seventh Framework Programme, 288409
Note

Validerad;2017;Nivå 2;2017-05-29 (rokbeg)

Available from: 2017-05-10 Created: 2017-05-10 Last updated: 2018-07-10Bibliographically approved
In thesis
1. EVOX-CPS: A Methodology For Data-Driven Optimization Of Building Operation
Open this publication in new window or tab >>EVOX-CPS: A Methodology For Data-Driven Optimization Of Building Operation
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Existing building stock’s energy efficiency must improve due to its significant proportion of the global energy consumption mix. Predictive building control promises to increase the efficiency of buildings during their operational phase and thus lead to a reduction of the lion’s share of buildings’ lifetime energy consumption. Predictive control complements other means to increase performance, such as refurbishments as well as modernization of systems.

This thesis contributes EVOX-CPS, a holistic methodology to develop data-driven predictive control for (existing) buildings and deploy the control in day-to-day use. EVOX-CPS evolves buildings into Cyber-Physical Systems and addresses the development of data-driven predictive control using computational methods. The thesis’ focus rests on accounting for the situation of existing buildings - which vary greatly regarding their physical characteristics, usage patterns, system installation, and instrumentation levels. The methodology addresses the aspect of building stock variety with its capability to flexibly adapt to different buildings’ characteristics, e.g., by supporting the integration of varying levels of pre-existing building instrumentation. Furthermore, EVOX-CPS supports using different data mining, regression, or control techniques (i) to strengthen the support for a variety of buildings, and (ii) to cater to researchers’ and practitioners’ differing skills, experiences, or preferences concerning different data analysis techniques. Through its flexibility, the methodology addresses a vast potential installation base and lowers the barriers for adoption in day-to-day use, e.g., by being able to leverage prior investments in building instrumentation and supporting different data-analysis techniques. At the same time, EVOX-CPS provides researchers and practitioners with comprehensive guidance relevant to their daily work. Besides, EVOX-CPS supports addressing a building’s known limitations in the daily operation, e.g., uncomfortable indoor conditions.

The experimentation in two real buildings validates the effectiveness of EVOX-CPS’ data-driven control with high reliability due to prolonged experimentation periods combined with applying energy normalization and inferential statistics. The experiments during routine heating system operation establish high confidence in the recorded effect sizes: the improvements in operational efficiency are profound and statistically significant. More specifically, the experiments of controlling the grass heating system of the soccer stadium Commerzbank Arena, Frankfurt, Germany, in two winters saved up to 66% (2014/2015) and 85% (2015/2016) of energy consumption. Extrapolation to an average heating season leads to expected savings of 775 MWh (148 t of CO2 emissions) and 1 GWh (197 t CO2), respectively. The experiments also show that EVOX-CPS allowed alleviating the known operational limitation of heating supply shortages which required nightly preheating in the stadium’s standard operating procedures. In another set of experiments, we applied the methodology to control the heating system of the Sierra Elvira School in Granada, Spain. The experimentation occurred during the regular class hours of 43 school days in winter 2015/2016. A first experiment demonstrated the possibility to lower consumption by one-third while maintaining indoor comfort. Another experiment raised average indoor temperatures by 2K with 5% additional energy consumption. Again, that illustrates EVOX-CPS’ capability to address a building’s known operational issues.

Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2018
Series
Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, ISSN 1402-1544
Keywords
Cyber-Physical Systems, Existing Buildings, Predictive Control, Sustainable Development, Energy Efficiency
National Category
Computer Sciences Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-67780 (URN)978-91-7790-059-7 (ISBN)978-91-7790-060-3 (ISBN)
Public defence
2018-04-27, Hörsal-A, Campus Skellefteå, Skellefteå, 08:30 (English)
Opponent
Supervisors
Available from: 2018-02-27 Created: 2018-02-26 Last updated: 2018-05-09Bibliographically approved

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

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Schmidt, Mischa

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