District Heating Load Prediction Algorithm Based on Feature Fusion LSTM Model

Guixiang Xue, Yu Pan, Tao Lin, Jiancai Song, Chengying Qi and Zhipan Wang
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Guixiang Xue: School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China
Yu Pan: School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China
Tao Lin: School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China
Jiancai Song: School of Information and Engineering, Tianjin University of Commerce, Tianjin 300134, China
Chengying Qi: School of Energy and Environment Engineering, Hebei University of Technology, Tianjin 300401, China
Zhipan Wang: School of International, Beijing University of Posts and Telecommunications, Beijing 100876, China

Energies, 2019, vol. 12, issue 11, 1-21

Abstract: The smart district heating system (SDHS) is an important element of the construction of smart cities in Northern China; it plays a significant role in meeting heating requirements and green energy saving in winter. Various Internet of Things (IoT) sensors and wireless transmission technologies are applied to monitor data in real-time and to form a historical database. The accurate prediction of heating loads based on massive historical datasets is the necessary condition and key basis for formulating an optimal heating control strategy in the SDHS, which contributes to the reduction in the consumption of energy and the improvement in the energy dispatching efficiency and accuracy. In order to achieve the high prediction accuracy of SDHS and to improve the representation ability of multi-time-scale features, a novel short-term heating load prediction algorithm based on a feature fusion long short-term memory (LSTM) model (FFLSTM) is proposed. Three characteristics, namely proximity, periodicity, and trend, are found after analyzing the heating load data from the aspect of the hourly time dimension. In order to comprehensively utilize the data’s intrinsic characteristics, three LSTM models are employed to make separate predictions, and, then, the prediction results based on internal features and other external features at the corresponding moments are imported into the high-level LSTM model for fusion processing, which brings a more accurate prediction result of the heating load. Detailed comparisons between the proposed FFLSTM algorithm and the-state-of-art algorithms are conducted in this paper. The experimental results show that the proposed FFLSTM algorithm outperforms others and can obtain a higher prediction accuracy. Furthermore, the impact of selecting different parameters of the FFLSTM model is also studied thoroughly.

Keywords: heat load prediction; deep learning; long short-term memory; feature fusion (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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