Prediction of residential district heating load based on machine learning: A case study

Abstract

Heating load prediction plays an important role in supporting the operation of a residential district energy station. To find out the most suitable prediction algorithm, seven popular machine learning algorithms are applied in a district heating load dataset from Shanghai, China. The data derived from electric power sensor, thermal sensor and meteorological sensor, weather forecast are combined into four input selections to evaluate model performance. Research on the length setting of prediction time horizon for prediction target and the length of historical time series data for input selection are also conducted to investigate corresponding performance changes. The results show that Support Vector Regression (SVR) performs best in Mean Average Percentage Error (MAPE) with 5.21%. XGBoost performs best in Root Mean Square Error (RMSE) with 59.75 kW. However, SVR, XGBoost and Long Short Term Memory neural network (LSTM) have similar performance. Further investigation shows that the consistent increase in the length of historical data sets does not help to improve the performance. The length of historical data sets is recommended to be 28 h. Meanwhile, the span of prediction horizon has slight influence on the performance. It is also found that model performance is primarily influenced by the weather forecasting data.