Multi-step ahead forecasting of heat load in district heating systems using machine learning algorithms

Abstract

Predicting next-day heat load curves is essential to guarantee sufficient heat supply and optimal operation of district heat systems (DHSs). Existing studies have mainly investigated one-step ahead forecasting methods, which can predict a single value at a future time step. To predict heat load curves, multi-step ahead forecasting methods are needed. This study proposes a machine learning-based framework for multi-step ahead DHS heat load forecasting. Specifically, support vector regression, deep neural network, and extreme gradient boosting (XGBoost) are respectively used as the base learner to develop forecasting models. Two multi-step ahead forecasting methods, i.e. direct strategy and recursive strategy, adopt the learnt models to generate predictions. A DHS in China is used as the case study to comprehensively assess the performance of these two forecasting strategies. Recursive strategy using the XGBoost-based forecasting model can achieve the most accurate and stable predictions with a value of 10.52% for the coefficient of variation of root mean square error. Furthermore, the modeling process of recursive strategy is much more convenient than that of direct strategy. The research shows that the recursive strategy is a better solution to multi-step ahead forecasting than the direct strategy with respect to accuracy, prediction stability, and modeling process.