Predicting hourly heating load in a district heating system based on a hybrid CNN-LSTM model

Abstract

Abstract Heat loads change dynamically with meteorological conditions and user demand, and the related accurate prediction algorithms are conducive to the realization of optimized automatic control and green heating. However, a traditional load forecasting algorithm exhibits low accuracy and is sensitive to data noise. Hence, it is difficult to satisfy the requirements of industrial applications via forecasting algorithms. The progress in big data technology leads to the construction of a smart district heating system (SDHS) with the next generation technology wherein artificial intelligence algorithms play the role of a chemical catalyst. The operation monitoring of heat exchange stations installed with various Internet of Things (IoT) sensors provides abundant data resources for heating load forecasting based on machine learning algorithms. A novel estimation model based on spatiotemporal hybrid convolution neural network long–short term memory (CNN–LSTM) is presented to predict the heating load more reasonably for the SDHS. This hybrid model deeply integrates the excellent feature extraction ability of CNN and LSTM in spatiotemporal two-dimensional feature space. Furthermore, CNN can extract the heating load s spatial characteristics and influencing factors, and LSTM can extract the heating load s temporal characteristics. Therefore, the CNN–LSTM algorithm can describe the complex change trend of the heating load and lead to a more accurate dynamic model of heating load with high nonlinearity and considerable thermal inertia delay. A detailed performance comparison was conducted between the CNN–LSTM and support vector machine (SVM) and ensemble learning algorithms, such as extra tree regression (ETR), random forest regression (RFR), gradient boosting regression (GBR), multiple layer perception (MLP). The experimental results showed the evident accuracy advantages of prediction performance based on the proposed CNN–LSTM algorithm. The MAPE evaluation indexes of the four heat exchange stations are distributed between 3.1% and 4.1%. Besides, this algorithm exhibits good adaptability for heating load data with different numerical ranges, which can satisfy the field engineering applications requirements in a more enhanced manner.