Multi-criteria comprehensive study on predictive algorithm of heating energy consumption of district heating station based on timeseries processing

Abstract

Refined control of district heating station relies on reasonable and accurate prediction of heating energy consumption. Due to the influence of building thermal inertia and time-delay of the district heating system, certain research work is necessary to thoroughly illustrate and analyze the impact of data timeseries processing on various prediction models. Four kinds of prediction algorithms were investigated and compared in this paper. Results showed that all of three timeseries processing methods, namely time feature construction, sliding window and building thermal inertia coefficient (CBTI), can improve the prediction accuracy of all four models and CBTI had the greatest impact on model accuracy improvement. Moreover, timeseries processing method has no limitation on the types of prediction model and it is a general method to improve the model accuracy. Time feature construction and sliding window had greater influence on the non-neural network models while CBTI was the opposite. In terms of model robustness, the robustness had been significantly improved after introducing timeseries processing except random forest, and the comprehensive robustness coefficient for the other three models had been reduced by about 95%. Recurrent neural network had extremely excellent robustness under different temporal granularity.