Heat load prediction through recurrent neural network in district heating and cooling systems

Abstract

As a heat load prediction method in district heating and cooling systems, the efficiency of layered neural networks has been shown, but there is a drawback that its prediction becomes less accurate in periods when the heat load is nonstationary. In this paper, we propose a new heat load prediction method superior to the existing method based on a layered neural network by using a recurrent neural network to deal with the dynamic variation of heat load as well as new input data in consideration of characteristics of heat load data.