Machine learning methods for prediction of hot water demands in integrated R744 system for hotels

摘要

Load forecasting can help modern energy systems achieve more efficient operation by means of more accuratepeak power shaving and more reliable control. This paper proposes a framework based on machine learningalgorithms to forecast the hot water usage for a Norwegian hotel. The framework is tested on the real data froman 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 canpredict demands with high accuracy. The hot water usage accounts for 52 % of hotels’ heat load, wherestrategic accumulation of the hot water storage can improve the overall system performance. Charging the hotwater 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 theintegrated 744 system.