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Context Sensitive Indoor Temperature Forecast for Energy Efficient Operation of Smart Buildings
Computer Science Faculty, University of Murcia.
Computer Science Faculty, University of Murcia.
NEC Laboratories Europe.
NEC Laboratories Europe.
Show others and affiliations
2015 (English)In: 2015 IEEE World Forum on Internet of Things (WF-IoT) (WF-IoT 2015), Piscataway, NJ: IEEE Communications Society, 2015, p. 705-710Chapter in book (Refereed)
Abstract [en]

This paper analyzes the potential of knowledge discovery from sensed data, which enables real-time systems monitoring, management, prediction and optimization in smart buildings. State of the art data driven techniques generate predictive short-term indoor temperature models based on real building data collected during daily operation. The most accurate results are achieved by the Bayesian Regularized Neural Network technique. Our results show that we are able to achieve a low relative predictive error for each room temperature in the range of 1.5% - 2.8% with low standard deviation of the residuals.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Communications Society, 2015. p. 705-710
Keywords [en]
Smart Buildings, Thermal Comfort, Data Modeling
National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
URN: urn:nbn:se:ltu:diva-21198DOI: 10.1109/WF-IoT.2015.7389140Local ID: c3642e20-4744-41c5-87e2-4a1a2a6e81b9OAI: oai:DiVA.org:ltu-21198DiVA, id: diva2:994243
Note
Upprättat; 2015; 20160118 (missch)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-02-26Bibliographically 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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