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The application of the attention deep learning model based on Particle Swarm Optimization in heating load prediction.

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Title: The application of the attention deep learning model based on Particle Swarm Optimization in heating load prediction.
Authors: Zhang, Xin1,2 (AUTHOR) zhangqqqf@126.com, Han, Jiliang1 (AUTHOR), Wang, Bo1 (AUTHOR), Pan, Qiang1 (AUTHOR), Zhao, Fang1 (AUTHOR), Zhang, Zhaowei3 (AUTHOR), Wang, Yida4 (AUTHOR)
Source: Journal of Building Performance Simulation. Jun2026, Vol. 19 Issue 4, p638-653. 16p.
Subjects: Particle swarm optimization, Heating load, Energy conservation, Meteorological databases, Long short-term memory, Ensemble learning, Deep learning
Geographic Terms: Harbin (China)
Abstract: How to predict heating load more accurately plays an important role in energy conservation and promoting scientific and green development of society. This study analyzes heating load data from two consecutive seasons (October 2021–April 2023) in Harbin, integrated with meteorological data, and proposes a novel hybrid model combining a Long Short-Term Memory Network (LSTM) with a Particle Swarm Optimization (PSO) algorithm and an Attention Mechanism. This model leverages LSTM's strength in extracting temporal features, the Attention Mechanism's capability for optimal feature weight allocation, and PSO for hyperparameter optimization. Comparative analysis against K-Nearest Neighbors (KNN), Support Vector Regression (SVR), Recurrent Neural Network (RNN), Attention-LSTM, PSO-LSTM, GA-LSTM, and DE-LSTM demonstrates that the proposed PSO-Attention-LSTM achieves superior performance, yielding the lowest Mean Squared Error (MSE) and Mean Absolute Error (MAE) while maintaining a comparable Coefficient of Determination (R²). Furthermore, an integrated model integrating the optimal KNN and PSO-Attention-LSTM was developed. This integrated model further enhances accuracy, reducing MAE and MSE by approximately 14.40% and 18.93% compared to KNN, and by 7.82% and 11.71% compared to the standalone PSO-Attention-LSTM, respectively. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:How to predict heating load more accurately plays an important role in energy conservation and promoting scientific and green development of society. This study analyzes heating load data from two consecutive seasons (October 2021–April 2023) in Harbin, integrated with meteorological data, and proposes a novel hybrid model combining a Long Short-Term Memory Network (LSTM) with a Particle Swarm Optimization (PSO) algorithm and an Attention Mechanism. This model leverages LSTM's strength in extracting temporal features, the Attention Mechanism's capability for optimal feature weight allocation, and PSO for hyperparameter optimization. Comparative analysis against K-Nearest Neighbors (KNN), Support Vector Regression (SVR), Recurrent Neural Network (RNN), Attention-LSTM, PSO-LSTM, GA-LSTM, and DE-LSTM demonstrates that the proposed PSO-Attention-LSTM achieves superior performance, yielding the lowest Mean Squared Error (MSE) and Mean Absolute Error (MAE) while maintaining a comparable Coefficient of Determination (R²). Furthermore, an integrated model integrating the optimal KNN and PSO-Attention-LSTM was developed. This integrated model further enhances accuracy, reducing MAE and MSE by approximately 14.40% and 18.93% compared to KNN, and by 7.82% and 11.71% compared to the standalone PSO-Attention-LSTM, respectively. [ABSTRACT FROM AUTHOR]
ISSN:19401493
DOI:10.1080/19401493.2026.2640405