  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 stadiums
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
stadiums 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 CO2 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 CO2) in an average winter. Beyond achieving significant energy
savings, the different control strategies also met the target temperature
levels to the satisfaction of the stadiums 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.
