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.

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.

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.

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.