Solar Power Generation Forecasting using a Hybrid LSTM-Linear Model with Multi-Head Attention 


Vol. 14,  No. 2, pp. 123-133, Feb.  2025
https://doi.org/10.3745/TKIPS.2025.14.2.123


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  Abstract

Due to the negative impact on the environment, the demand for solar energy, which can effectively replace fossil fuels, is increasing. In order to operate solar energy efficiently, deep learning has been used recently to predict future power generation, but it is still a challenge to provide accurate predictions because the power generation greatly depends on external factors such as weather and time of day. In this paper, we propose a hybrid model that combines LSTM and Linear models using Multi-Head Attention to provide more accurate predictions. The proposed model can improve prediction accuracy and learn richer time series features because each Linear model can capture trends and irregularities in time series features. We conducted extensive experiments, and the results showed that the proposed model outperformed other prediction models by about 10%, and the ablation study confirmed that combining three models based on Multi-Head Attention is the most effective way to consider trends and irregularities.

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[IEEE Style]

H. Jin, D. J. Richter, M. I. Bappi, K. Kim, "Solar Power Generation Forecasting using a Hybrid LSTM-Linear Model with Multi-Head Attention," The Transactions of the Korea Information Processing Society, vol. 14, no. 2, pp. 123-133, 2025. DOI: https://doi.org/10.3745/TKIPS.2025.14.2.123.

[ACM Style]

Hyeonseok Jin, David J. Richter, MD Ilias Bappi, and Kyungbaek Kim. 2025. Solar Power Generation Forecasting using a Hybrid LSTM-Linear Model with Multi-Head Attention. The Transactions of the Korea Information Processing Society, 14, 2, (2025), 123-133. DOI: https://doi.org/10.3745/TKIPS.2025.14.2.123.

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