SHORT TERM SOLAR RADIATION FORECASTING BASED ON ICEEMDAN-LSTM AND RESIDUAL ATTENTION

Zang Haixiang; Zhang Yue; Cheng Lilin; Liu Jingxuan; Wei Zhinong; Sun Guoqiang

doi:10.19912/j.0254-0096.tynxb.2022-1761

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Acta Energiae Solaris Sinica ›› 2023, Vol. 44 ›› Issue (12) : 175-181. DOI: 10.19912/j.0254-0096.tynxb.2022-1761

SHORT TERM SOLAR RADIATION FORECASTING BASED ON ICEEMDAN-LSTM AND RESIDUAL ATTENTION

Zang Haixiang, Zhang Yue, Cheng Lilin, Liu Jingxuan, Wei Zhinong, Sun Guoqiang

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Abstract

In order to improve the accuracy of short-term solar radiation forecasting, a novel forecasting method based on ICEEMDAN-LSTM and residual attention is proposed. The original radiation sequence is first decomposed into multiple modal components by the improved complete ensemble empirical modal decomposition with adaptive noise. Residual attention is introduced to reconstruct the original meteorological features at the same time. Long short-term memory（LSTM） network is then utilized to extract temporal features of the two parts, respectively. After that, temporal features of each part are concatenated as inputs of a multi-layer perception, which can generate one-hour-ahead prediction results of global horizontal irradiance. Experimental results demonstrate that the proposed method can capture the fluctuations and abrupt changes of the irradiance series and is able to consider the importance of different meteorological features for the prediction task. The proposed method is proved to be effective in improving the prediction accuracy of short-term solar irradiance.

Key words

solar radiation / forecasting / deep learning / ICEEMDAN / long short-term memory network / residual attention

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Zang Haixiang, Zhang Yue, Cheng Lilin, Liu Jingxuan, Wei Zhinong, Sun Guoqiang. SHORT TERM SOLAR RADIATION FORECASTING BASED ON ICEEMDAN-LSTM AND RESIDUAL ATTENTION[J]. Acta Energiae Solaris Sinica. 2023, 44(12): 175-181 https://doi.org/10.19912/j.0254-0096.tynxb.2022-1761

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