SHORT-TERM PHOTOVOLTAIC POWER INTERVAL PREDICTION MODEL BASED ON MODWT-CEEMDAN-LSTM

Chen Chuanyu; Xiong Guojiang; Fang Houkang; Luo Yingxun

doi:10.19912/j.0254-0096.tynxb.2023-1578

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (2) : 416-424. DOI: 10.19912/j.0254-0096.tynxb.2023-1578

SHORT-TERM PHOTOVOLTAIC POWER INTERVAL PREDICTION MODEL BASED ON MODWT-CEEMDAN-LSTM

Chen Chuanyu¹, Xiong Guojiang^1,2, Fang Houkang¹, Luo Yingxun¹

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Abstract

In response to the volatility, randomness, and intermittency of photovoltaic power, this study proposes a short-term interval prediction model based on the maximum overlapping discrete wavelet transform（MODWT）, complementary empirical mode decomposition with adaptive noise（CEEMDAN）, and long short-term memory network（LSTM）. Firstly, MODWT and CEEMDAN are used to decompose the PV power time series quadratically to obtain the intrinsic mode functions （IMF） components, Then,these IMF components are inputted into the LSTM for component prediction and the component prediction results are reconstructed to obtain the point prediction result; Finally, quantile regression is used to model the point prediction results and the interval prediction results are obtained. Finally, the interval prediction results are obtained by modeling the point prediction results with quantile regression. Practical examples show that the combination of the time-frequency domain decomposition method and the frequency domain decomposition method makes the model show excellent robustness and accuracy in the point prediction and interval prediction of PV power under the three weather conditions.

Key words

PV power / prediction / deep learning / long short-term memory / maximal overlap discrete wavelet transform / complete ensemble empirical mode decomposition with adaptive noise

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Chen Chuanyu, Xiong Guojiang, Fang Houkang, Luo Yingxun. SHORT-TERM PHOTOVOLTAIC POWER INTERVAL PREDICTION MODEL BASED ON MODWT-CEEMDAN-LSTM[J]. Acta Energiae Solaris Sinica. 2025, 46(2): 416-424 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1578

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