A novel combined model for heat load prediction in district heating systems

Abstract

Accurate heat load prediction is essential for improving the operational efficiency of district heating systems (DHSs). Numerous heat load prediction models have been proposed to improve the accuracy and stability. However, previous prediction models are limited by training data and hyperparameters and cannot obtain accurate and stable prediction results. This study proposed a combined prediction model to overcome the limitations of a single model. In the proposed model, four classical single models were selected as individual models for prediction. Subsequently, the minimum sum of squares of the combined prediction errors algorithm was used to calculate the weighting coefficient of each individual model. The final prediction results were obtained by combining the prediction results of the four models. The 1-h historical operation data from three heat substations in a DHS in northeast China were adopted to evaluate the performance of the proposed model. The multi-step prediction experiment results of the three datasets revealed that the average mean absolute percentage error values of the proposed model for the one-step, two-step, and three-step predictions reached 2.6%, 4.9%, and 7.0%, respectively; these percentages were significantly lower than those of the four individual models and three representative combined models. Therefore, the proposed model has better prediction performance than previous models in terms of accuracy and stability and can provide a theoretical basis for the precise regulation of DHSs.