Load forecasting of district heating system based on improved FB-Prophet model

Accurate load forecasting of the district heating network (DHN) is essential to guarantee effective energy production, distribution, and rational utilization. An improved Facebook-Prophet (FB-Prophet) model with additional positional encoding layers has been developed to forecast the DHN heat consumption. The accuracy of univariate and multivariate FB-Prophet models is evaluated; this paper also evaluates the optimum training dataset length. To explore the performance of the improved FB-Prophet model in heating load forecasting tasks, another seven machine learning models, namely FB-Prophet, DeepVAR, long-short term memory, extreme gradient boosting, multilayer perceptron, recurrent neural network, and support vector regression are used for comparison. The historical heating load, outside temperature, relative humidity, speed of wind, direction of wind, and weather type of a DHN in Serbia are utilized to extensively investigate the effectiveness of the improved FB-Prophet model. The prediction outcomes of all the models are thoroughly analyzed. The results indicate that the improved FB-Prophet model can generate the most precise and consistent predictions and it showed better results for sparse DHN data. The prediction curve is fitted to the trend of hourly DHN consumption change, which can play an effective guiding function in the distribution of heat.