Selection and Evaluation of influencing Parameters for Heat Load Forecasting Model

School of Environment and Energy Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China

^* Corresponding author’s e-mail: lirui@bucea.edu.cn

Abstract

This paper analyses the factors affecting the heating consumption of a heating substation. The input parameters of neural network prediction model are analysed and selected. The average absolute error, average absolute percentage error, and mean square error are used to evaluate the effect of the prediction model. The results show that when the model input parameters are the maximum outdoor temperature, the average outdoor temperature, the average temperature difference between the primary supply and return of domestic hot water, the heating load in the previous three days, the heating load in the previous two days, the heating load in the previous day and when the model input parameters are the maximum outdoor temperature, the minimum outdoor temperature, the average outdoor temperature, the average temperature difference between the primary supply and return of domestic hot water, the heating load of the previous three days, the heating load of the previous two days, the heating load of the previous day, the effects are better.