Paper
10 October 2023 A novel heating load forecasting method based on information entropy attention mechanism
Jianhua Yin, Guobin Zhang, Hongyan Huo, Qian Zhang
Author Affiliations +
Proceedings Volume 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023); 127993T (2023) https://doi.org/10.1117/12.3006470
Event: 3rd International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 2023, Kuala Lumpur, Malaysia
Abstract
Due to the high delay and large inertia characteristics, there are issues with inefficient regulation and energy waste in district heating systems. Improving the predictive ability of regional heating load can help improve the accuracy of thermal network control. In order to improve the accuracy of heating load prediction, this paper proposes a new heating load prediction algorithm, STL-IEA-LSTM, which combines LSTM model with information entropy attention mechanism to separately model the data modeling of heating load after STL algorithm decomposition, so as to enhance the prediction ability of heating load. The experimental evaluation conducted on the operating data of a heating station in Shijiazhuang during the heating season demonstrated that the STL-IEA-LSTM combination model constructed in this study achieved a MAPE of 0.7031%, indicating its superior performance in predicting heating load. Comparative analysis with other models, including STL-LSTM, entropy-based LSTM, classic LSTM, XGBoost, and MLP, further confirmed the effectiveness of the proposed method in achieving high prediction accuracy.
(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Jianhua Yin, Guobin Zhang, Hongyan Huo, and Qian Zhang "A novel heating load forecasting method based on information entropy attention mechanism", Proc. SPIE 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 127993T (10 October 2023); https://doi.org/10.1117/12.3006470
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KEYWORDS
Data modeling

Performance modeling

Engineering

Machine learning

Mathematical modeling

Neural networks

Reflection

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