Applied Energy has published our work Using machine learning techniques for occupancy-prediction-based cooling control in office buildings.
We propose a demand-driven control strategy that automatically responds to occupants’ energy-related behavior for reducing energy consumption and maintains room temperature for occupants with similar performances as a static cooling. This learning-based approach reduces the need for human intervention in the cooling system’s control. The proposed strategy was applied to control the cooling system of the office building under real-world conditions, and results report between 7% and 52% energy savings compared to the conventionally-scheduled cooling systems.
Y. Peng, A. Rysanek, Z. Nagy, and A. Schlueter Using machine learning techniques for occupancy-prediction-based cooling control in office buildings Applied Energy, Vol. 211, pp. 1343–1358, 2018