Machine learning methods for prediction of hot water demands in integrated R744 system for hotels
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Load forecasting can help modern energy systems achieve more efficient operation by means of more accurate peak power shaving and more reliable control. This paper proposes a framework based on machine learning algorithms to forecast the hot water usage for a Norwegian hotel. The framework is tested on the real data from an integrated R744 HVAC and domestic hot water system with a 6 m3 thermal storage. Recorded operational data and ambient temperatures are utilized to build several forecasting models that can predict demands with high accuracy. The hot water usage accounts for 52 % of hotels’ heat load, where strategic accumulation of the hot water storage can improve the overall system performance. Charging the hot water storage according to three-hour-ahead demand predictions presents significant savings potential. This work can facilitate a demand management strategy and thus improve the energy efficiency of the integrated 744 system.