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

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