Abstract-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 nonstationary. 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.
In REF , recurrent neural networks (RNNs) are used for heat load prediction in district heating and cooling systems.
Heat load prediction through recurrent neural network in district heating and cooling systems
2008 IEEE International Conference on Systems, Man and Cybernetics
2008 IEEE International Conference on Systems, Man and Cybernetics
Computer Science