Heat Load Prediction in District Heating and Cooling Systems through a Recurrent Neural Network with Data Characteristics

Heat load prediction in district heating and cooling (DHC) systems is one of the key technologies for economical and safe operations of DHC systems. The heat load prediction method using a three-layered neural network proposed by the authors has been used in an actual DHC plant on a trial basis. However, there exists a drawback that its prediction becomes less accurate in periods when the heat load is nonstationary. In this paper, we improve the heat load prediction method through the introduction of a recurrent neural network for adapting the dynamical variation of heat load together with a new kind of input data in consideration of the characteristics of heat load data. In order to show the efficiency of the proposed method, we carry out several numerical experiments using actual heat load data.