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EVOX-CPS: A Methodology For Data-Driven Optimization Of Building Operation
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. NEC Laboratories Europe.
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 [en]
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: urn:nbn:se:ltu:diva-67780ISBN: 978-91-7790-059-7 (print)ISBN: 978-91-7790-060-3 (electronic)OAI: oai:DiVA.org:ltu-67780DiVA, id: diva2:1185930
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
List of papers
1. Predictability of energy characteristics for cooling, ventilation and heating systems in sports facilities
Open this publication in new window or tab >>Predictability of energy characteristics for cooling, ventilation and heating systems in sports facilities
2015 (English)In: I2015 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT): Washington, DC , 18-20 Feb 2015, Piscataway, NJ: IEEE Communications Society, 2015Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we analyze operational energy data of the cooling, ventilation and heating systems of the professional soccer stadium Commerzbank Arena in Frankfurt, Germany. We analyze data collected over a six month period in 2014 statistically and show that depending on the stadium's operational context consumption patterns vary largely among the different systems resulting in very different behaviors. The results provide insights into what drives the energy consumption for different systems of a large commercial sports facility: the static heating system is purely dependent on outside air temperature, ventilation exhibits a pronounced daily consumption pattern irrespective of the temperature and cooling is driven by a combination of event operation and air temperature. These insights will allow us to predict, plan and balance the energy demands of different subsystems more accurately, resulting in energetic improvements of the stadium operation in the form of load shedding while maintaining the systems' service levels.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Communications Society, 2015
Keywords
building services, cooling, energy consumption, facilities, heating, load shedding, sport, ventilation, Commerzbank arena, Frankfurt, Germany, air temperature, energy characteristic predictability, energy demand, soccer stadium, sports facility, static heating system, Buildings, Business, Context, Energy consumption, Heating, Ventilation, Context Awareness, Control Systems, Energy Management, Green Buildings, HVAC, Sports Stadium
National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
urn:nbn:se:ltu:diva-30814 (URN)10.1109/ISGT.2015.7131848 (DOI)84939137908 (Scopus ID)4c4bad1b-74a4-4cec-a1e7-bc03aa73fce2 (Local ID)978-1-4799-1785-3 (ISBN)4c4bad1b-74a4-4cec-a1e7-bc03aa73fce2 (Archive number)4c4bad1b-74a4-4cec-a1e7-bc03aa73fce2 (OAI)
Conference
IEEE Power & Energy Society Innovative Smart Grid Technologies Conference : 18/02/2015 - 20/02/2015
Note
Upprättat; 2015; 20150921 (missch)Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2018-02-26Bibliographically approved
2. The Energy Efficiency Problematics in Sports Facilities: Identifying Savings in Daily Grass Heating Operation
Open this publication in new window or tab >>The Energy Efficiency Problematics in Sports Facilities: Identifying Savings in Daily Grass Heating Operation
2015 (English)In: Proceedings of the ACM/IEEE Sixth International Conference on Cyber-Physical Systems, New York: ACM Digital Library, 2015, p. 189-197Conference paper, Published paper (Refereed)
Abstract [en]

Recently, reflections on modern sports stadiums' environmental impacts have gained substantial attention. Large-scale stadiums of e.g. professional soccer teams are characterized by having installations of grass heating systems serving the crucial commercial asset and at the same being the sub-system with the highest yearly thermal energy consumption. Public buildings of this size imply situation-specific operational modes combined with high levels of safety and comfort requirements. In this paper we provide a first study on the energy savings potential of a professional soccer stadium's grass heating system during day-to-day operation. In practice, limited heating capacities of the arena have to be adhered to, which causes the current operation to often result in under-performance of other, less critical facility units. Our analysis of dynamic operational and contextual data serves as foundation for long-term energy efficiency measures. We study relevant parameters related to the current control schemes and the stadium's context. Concretely, the grass root temperature as critical observable is studied with respect to weather conditions and the resulting thermal behavior. We provide an improved control strategy and quantify the anticipated savings of this strategy to be as high as 34% compared to the last heating season. For the future, the documented thermal characteristics will enable the formulation of more advanced control strategies to positively influence the grass heating operation. This will lead to further improvements in balancing the heating demand across all thermal facility sub-systems by integrating operational context with forecasts of the thermal behavior in the future.

Place, publisher, year, edition, pages
New York: ACM Digital Library, 2015
Keywords
context awareness, control systems, cyber-physical systems, energy management, grass heating, smart buildings, sports stadium
National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
urn:nbn:se:ltu:diva-28073 (URN)10.1145/2735960.2735978 (DOI)1b94dc51-429a-475c-aa01-fc581b894ebf (Local ID)978-1-4503-3455-6 (ISBN)1b94dc51-429a-475c-aa01-fc581b894ebf (Archive number)1b94dc51-429a-475c-aa01-fc581b894ebf (OAI)
Conference
ACM/IEEE Sixth International Conference on Cyber-Physical Systems : 14/04/2014 - 16/04/2015
Note
Upprättat; 2015; 20150921 (missch)Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2018-02-26Bibliographically approved
3. Energy Efficiency Gains in Daily Grass Heating Operation of Sports Facilities through Supervisory Holistic Control
Open this publication in new window or tab >>Energy Efficiency Gains in Daily Grass Heating Operation of Sports Facilities through Supervisory Holistic Control
2015 (English)In: Buildsys'15: 2nd ACM International Conference on Embedded Systems for Energy-Efficient Built, New York: ACM Digital Library, 2015, p. 85-94Conference paper, Published paper (Refereed)
Abstract [en]

In recent reflections on environmental impacts of buildings, medium to large scale sports stadiums have gained substantial attention. These stadiums of e.g. professional soccer teams are characterized by special system installations like grass heating systems serving the crucial commercial asset(s) and by event-driven usage patterns. Public buildings of this size imply situation-specific operational modes combined with high levels of safety and comfort requirements. In this paper we provide experimental verification of the energy savings potential of a professional soccer stadium's grass heating system during day-to-day operation. Our supervisory holistic control based on state of the art information and communication technology (ICT) is verified by seven experiments which we executed within the real operational setup of the Commerzbank Arena in Frankfurt, Germany. Our experiments operated different control strategies of increasing complexity. In winter 2014/2015 we achieved weather normalized energy savings of more than 56% compared to the last heating season. In an average heating season this would amount to savings of approximately 780 MWh and 150 t CO2$. At the same time we violated minimum temperature targets less than 6% of the time. These results stress the feasibility and benefits of applying holistic context-aware control strategies to large scale legacy consumption systems using supervisory ICT platforms. We demonstrate significant efficiency improvements and establish a new energy baseline that future control strategy evolutions will have to benchmark against.

Place, publisher, year, edition, pages
New York: ACM Digital Library, 2015
National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
urn:nbn:se:ltu:diva-20556 (URN)10.1145/2821650.2821661 (DOI)000380608700010 ()5ea67e1e-445e-47b5-a9c2-2e1866eaea16 (Local ID)978-1-4503-3981-0 (ISBN)978-1-4503-3981-0 (ISBN)5ea67e1e-445e-47b5-a9c2-2e1866eaea16 (Archive number)5ea67e1e-445e-47b5-a9c2-2e1866eaea16 (OAI)
Conference
2nd ACM International Conference on Embedded Systems for Energy-Efficient Built, Seoul, South Korea, Nov 4-5 2015
Note

Validerad; 2016; Nivå 1; 2016-10-06 (andbra)

Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-02-26Bibliographically approved
4. Cyber-Physical System For Energy Efficient Stadium Operation: Methodology And Experimental Validation
Open this publication in new window or tab >>Cyber-Physical System For Energy Efficient Stadium Operation: Methodology And Experimental Validation
Show others...
2018 (English)In: ACM Transactions on Cyber-Physical Systems, ISSN 2378-962XArticle in journal (Refereed) In press
Abstract [en]

The environmental impacts of medium to large scale buildings receive substantial attention in research,industry, and media. This paper studies the energy savings potential of a commercial soccer stadium duringday-to-day operation. Buildings of this kind are characterized by special purpose system installations likegrass heating systems and by event-driven usage patterns. This work presents a methodology to holisticallyanalyze the stadium’s characteristics and integrate its existing instrumentation into a Cyber-PhysicalSystem, enabling to deploy different control strategies flexibly. In total, seven different strategies for controllingthe studied stadium’s grass heating system are developed and tested in operation. Experiments inwinter season 2014/2015 validated the strategies’ impacts within the real operational setup of the CommerzbankArena, Frankfurt, Germany. With 95% confidence, these experiments saved up to 66% of mediandaily weather-normalized energy consumption. Extrapolated to an average heating season, this correspondsto savings of 775 MWh and 148 t of CO2 emissions. In winter 2015/2016 an additional predictive nighttimeheating experiment targeted lower temperatures, which increased the savings to up to 85%, equivalent to1 GWh (197 t CO2) in an average winter. Beyond achieving significant energy savings, the different controlstrategies also met the target temperature levels to the satisfaction of the stadium’s operational staff. Whilethe case study constitutes a significant part, the discussions dedicated to the transferability of this workto other stadiums and other building types show that the concepts and the approach are of general nature.Furthermore, this work demonstrates the first successful application of Deep Belief Networks to regress andpredict the thermal evolution of building systems.

Place, publisher, year, edition, pages
ACM Digital Library, 2018
Keywords
Stadium Operation, Under-soil Heating, Statistical Inference, Predictive Control, Deep Belief Network, Energy Efficiency, System Modeling
National Category
Computer Systems Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
urn:nbn:se:ltu:diva-67705 (URN)
Available from: 2018-02-20 Created: 2018-02-20 Last updated: 2018-04-17
5. Context Sensitive Indoor Temperature Forecast for Energy Efficient Operation of Smart Buildings
Open this publication in new window or tab >>Context Sensitive Indoor Temperature Forecast for Energy Efficient Operation of Smart Buildings
Show others...
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
Keywords
Smart Buildings, Thermal Comfort, Data Modeling
National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
urn:nbn:se:ltu:diva-21198 (URN)10.1109/WF-IoT.2015.7389140 (DOI)c3642e20-4744-41c5-87e2-4a1a2a6e81b9 (Local ID)c3642e20-4744-41c5-87e2-4a1a2a6e81b9 (Archive number)c3642e20-4744-41c5-87e2-4a1a2a6e81b9 (OAI)
Note
Upprättat; 2015; 20160118 (missch)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-02-26Bibliographically approved
6. Smart Buildings as Cyber-Physical Systems:Data-Driven Predictive Control Strategies for Energy Efficiency
Open this publication in new window or tab >>Smart Buildings as Cyber-Physical Systems:Data-Driven Predictive Control Strategies for Energy Efficiency
2018 (English)In: Renewable & sustainable energy reviews, ISSN 1364-0321, E-ISSN 1879-0690, Vol. 90, p. 742-756Article in journal (Refereed) Published
Abstract [en]

Due to its significant contribution to global energy usage and the associated greenhouse gas emissions, existing buildingstock’s energy efficiency must improve. Predictive building control promises to contribute to that by increasing theefficiency of building operations. Predictive control complements other means to increase performance such as refurbishmentsas well as modernizations of systems. This survey reviews recent works and contextualizes these with thecurrent state of the art of interrelated topics in data handling, building automation, distributed control, and semantics.The comprehensive overview leads to seven research questions guiding future research directions.

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
Energy Efficiency, Predictive Control, Cyber-Physical System, Existing Buildings
National Category
Computer Systems Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-67706 (URN)10.1016/j.rser.2018.04.013 (DOI)2-s2.0-85045248317 (Scopus ID)
Note

Validerad;2018;Nivå 2;2018-04-26 (andbra)

Available from: 2018-02-20 Created: 2018-02-20 Last updated: 2018-06-11Bibliographically approved
7. Optimizing legacy building operation: the evolution into data-driven predictive cyber-physical systems
Open this publication in new window or tab >>Optimizing legacy building operation: the evolution into data-driven predictive cyber-physical systems
Show others...
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
Keywords
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:nbn:se:ltu:diva-63299 (URN)10.1016/j.enbuild.2017.05.002 (DOI)000404705000021 ()
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: 2018-02-26Bibliographically approved

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