Energy Efficiency Gains in Daily Grass Heating Operation of Sports Facilities through Supervisory Holistic Control

Schmidt, Mischa; Schülke, Anett; Venturi, Alberto; Kurpatov, Roman

doi:10.1145/2821650.2821661

Cited by 3 publications

(7 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The CPS issues appropriate control commands to the BMS to enact these using the lower automation infrastructure layers. This paper builds on earlier findings in [Schmidt et al 2015b;Schmidt et al 2015a] and extends these as follows:…”

Section: Related Work Relation To Earlier Worksupporting

confidence: 80%

“…- [Schmidt et al 2015a] formulates the seven different algorithms to control the stadium's grass heating system and provides their experimental validation -the winter 2014/2015 experiments. It provides descriptive statistics of weather-normalized energy use and grass root temperatures, demonstrating significant savings compared to the status quo operation.…”

Section: Related Work Relation To Earlier Workmentioning

confidence: 99%

See 1 more Smart Citation

Cyber-Physical System for Energy-Efficient Stadium Operation

Schmidt

Schülke²,

Venturi³

et al. 2018

ACM Trans. Cyber-Phys. Syst.

Self Cite

View full text Add to dashboard Cite

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 during day-to-day operation. Buildings of this kind are characterized by special purpose system installations like grass heating systems and by event-driven usage patterns. This work presents a methodology to holistically analyze the stadium's characteristics and integrate its existing instrumentation into a Cyber-Physical System, enabling to deploy different control strategies flexibly. In total, seven different strategies for controlling the studied stadium's grass heating system are developed and tested in operation. Experiments in winter season 2014/2015 validated the strategies' impacts within the real operational setup of the Commerzbank Arena, Frankfurt, Germany. With 95% confidence, these experiments saved up to 66% of median daily weather-normalized energy consumption. Extrapolated to an average heating season, this corresponds to savings of 775 MWh and 148 t of CO 2 emissions. In winter 2015/2016 an additional predictive nighttime heating experiment targeted lower temperatures, which increased the savings to up to 85%, equivalent to 1 GWh (197 t CO 2 ) in an average winter. Beyond achieving significant energy savings, the different control strategies also met the target temperature levels to the satisfaction of the stadium's operational staff. While the case study constitutes a significant part, the discussions dedicated to the transferability of this work to 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 and predict the thermal evolution of building systems.

show abstract

Section: Related Work Relation To Earlier Worksupporting

confidence: 80%

Section: Related Work Relation To Earlier Workmentioning

confidence: 99%

Cyber-Physical System for Energy-Efficient Stadium Operation

Schmidt

Schülke²,

Venturi³

et al. 2018

ACM Trans. Cyber-Phys. Syst.

Self Cite

View full text Add to dashboard Cite

show abstract

“…provided via the Internet, may improve the efficiency of predictive control strategies. For example, [82] deployed its cyber-representation for predictive control actions on a separate server by using the BACnet/IP protocol for BMS integration and accessed additional weather information provided by [83]. Figure 2 illustrates this concept.…”

Section: Relation To Building Automationmentioning

confidence: 99%

“…Studying a more exotic heating system, [82] demonstrated weather-normalized thermal energy savings of 56% over a winter season operating a soccer stadium's grass heating system -a major heat sink. This study experiments with a variety of control heuristics, e.g.…”

Section: Data-driven 341 Neural Networkmentioning

confidence: 99%

“…In the SCADA of a common medium or large-scale building, a considerable number of variables is available for study. For example, in [82] the soccer stadium's BMS provides access to approximately 13,000 variables. The binary question whether or not to include a feature in combination with other variables yields 2 𝑁 possible combinations.…”

Section: Feature Selectionmentioning

confidence: 99%

See 1 more Smart Citation

Smart buildings as Cyber-Physical Systems: Data-driven predictive control strategies for energy efficiency

Schmidt

Åhlund

2018

Renewable and Sustainable Energy Reviews

Self Cite

107

View full text Add to dashboard Cite

Due to its significant contribution to global energy usage and the associated greenhouse gas emissions, existing building stock's energy efficiency must improve. Predictive building control promises to contribute to that by increasing the efficiency of building operations. Predictive control complements other means to increase performance such as refurbishments as well as modernizations of systems. This survey reviews recent works and contextualizes these with the current 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.

show abstract