Abstract
Accurate heat load prediction algorithm provides important support for the stable and efficient operation of smart district heating system (SDHS) and helps to realize energy saving and consumption reduction. However, previous researches on heat load prediction are mostly carried out on various regression analyses and modeling prediction, without considering the inherent time delay and spatial dependence between heat exchange stations during regulation. Therefore, a novel heat load prediction model based on the hybrid spatial-temporal attention long short-term memory (STALSTM) is proposed. The STALSTM model introduces the spatial dependence characteristics of heating pipe network into the heat load prediction model, and the influencing factors of heat consumption are considered comprehensively from the time and space dimensions. Then, the LSTM algorithm is used to memory the information of historical data sequence, and the attention mechanism is used to realize the adaptive estimation of the characteristic weight of each influencing factor, which improves the prediction accuracy. And in order to verify the effectiveness of the proposed model, a detailed experimental comparison is made between the STALSTM model and the state-of-the-art algorithms. The results show that the STALSTM model has the best prediction accuracy, and the correctness of introducing the spatial-temporal characteristics and the attention mechanism is also proved.
Highlights
With the rapid development of urbanization in China, the scope of central heating is expanding
district heating systems (DHS) is a non-linear system with wide spatial distribution of pipeline topology, and there is a time delay caused by building thermal inertia
In order to improve the accuracy of heat load prediction, a novel hybrid spatial-temporal attention-long short-term memory (LSTM) model is proposed in this paper
Summary
With the rapid development of urbanization in China, the scope of central heating is expanding. In Ref [6], a data-driven machine learning model is proposed to predict the heat load of buildings in DHS. Some scholars have introduced spatial characteristics into prediction researches and obtained good prediction results, which have great reference value It mainly involves radar signal [23], weather information [24], wind speed [25] and traffic flow [26], [27] prediction. It provides that the introduction of spatial characteristics is helpful to improve the prediction performance of the model, and provides a theoretical basis for introducing spatial characteristics into the study of heat load prediction.
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