Title :
Energy efficient building climate control using Stochastic Model Predictive Control and weather predictions
Author :
Oldewurtel, F. ; Parisio, A. ; Jones, C.N. ; Morari, M. ; Gyalistras, D. ; Gwerder, M. ; Stauch, V. ; Lehmann, B. ; Wirth, K.
Author_Institution :
Dept. of Electr. Eng., ETH Zurich, Zurich, Switzerland
fDate :
June 30 2010-July 2 2010
Abstract :
One of the most critical challenges facing society today is climate change and thus the need to realize massive energy savings. Since buildings account for about 40% of global final energy use, energy efficient building climate control can have an important contribution. In this paper we develop and analyze a Stochastic Model Predictive Control (SMPC) strategy for building climate control that takes into account weather predictions to increase energy efficiency while respecting constraints resulting from desired occupant comfort. We investigate a bilinear model under stochastic uncertainty with probabilistic, time varying constraints. We report on the assessment of this control strategy in a large-scale simulation study where the control performance with different building variants and under different weather conditions is studied. For selected cases the SMPC approach is analyzed in detail and shown to significantly outperform current control practice.
Keywords :
air conditioning; bilinear systems; building management systems; climatology; energy conservation; energy consumption; predictive control; probability; space heating; stochastic systems; ventilation; weather forecasting; SMPC strategy; air conditioning; bilinear model; building variant; climate change; control performance; energy efficient building climate control; global final energy use; heating; large-scale simulation; massive energy saving; occupant comfort; probabilistic constraint; stochastic model predictive control; stochastic uncertainty; time varying constraint; ventilation; weather condition; weather prediction; Actuators; Automatic control; Energy efficiency; Mathematical model; Predictive control; Predictive models; Stochastic processes; Temperature control; Uncertainty; Weather forecasting;
Conference_Titel :
American Control Conference (ACC), 2010
Conference_Location :
Baltimore, MD
Print_ISBN :
978-1-4244-7426-4
DOI :
10.1109/ACC.2010.5530680