Hourly Heat Load Prediction Model Based on Temporal Convolutional Neural Network

Smart district heating system (SDHS) is an important way to realize green energy saving and comfortable heating in the future, which is conducive to improving energy utilization efficiency and reducing pollution emissions. The accurate prediction algorithm of heating load plays an important role in on-demand heat supply, however, the heating load prediction is a complicated nonlinear optimization problem, and the prediction accuracy is limited due to the poor nonlinear expression ability of the traditional prediction algorithms. This paper proposes a heating load prediction model based on temporal convolutional neural network (TCN), which implements the rapid extraction of complex data features due to the integration of both the parallel feature processing of convolution neural network (CNN) and the time-domain modeling capability of recurrent neural network (RNN). The engineering data of four heat exchange stations located in Anyang, China in the 2018 heating season is used to evaluate and verify the performance of proposed prediction algorithm based on TCN, and the comprehensive comparisons with state-of-the-art algorithms, such as RFR, ETR, GBR, SVR, NuSVR, SGD, Bagging, Boosting, MLP, RNN, LSTM, etc., were analyzed carefully. The experimental results shown that the proposed heat load prediction algorithm based on TCN has performance superiority.