PHOTOVOLTAIC POWER FORECASTING BASED ON MAXIMUM OVERLAP DISCRETE WAVELET TRANSFORM AND DEEP LEARNING

Ma Lele, Kong Xiaobing, Guo Lei, Liu Yuanyan, Liu Xiangjie

Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (5) : 576-583.

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Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (5) : 576-583. DOI: 10.19912/j.0254-0096.tynxb.2022-1993

PHOTOVOLTAIC POWER FORECASTING BASED ON MAXIMUM OVERLAP DISCRETE WAVELET TRANSFORM AND DEEP LEARNING

  • Ma Lele, Kong Xiaobing, Guo Lei, Liu Yuanyan, Liu Xiangjie
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Abstract

Aiming at the non-stationary characteristics of PV power time series, this paper proposes a hybrid PV power forecasting model based on maximum overlap discrete wavelet transform (MODWT) and long short-term memory network (LSTM). First, Pearson correlation coefficient is used to identify important meteorological factors while MODWT is used to decompose the historical PV power series. The selected meteorological factors and the decomposed stationary subsequences are combined to form the input of each LSTM network. The sub-sequence prediction results of each LSTM network are integrated and reconstructed to the final PV power prediction results. The complete reconstruction of MODWT algorithm established in this paper is analyzed at the theoretical level, and the range of learning rate to ensure the convergence of the prediction network is derived based on Lyapunov stability theorem. The simulation results show that this proposed forecasting model has the obvious advantages in forecasting accuracy and robustness.

Key words

photovoltaic power forecasting / long short-term memory network / non-stationary time series decomposition / convergence of prediction network

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Ma Lele, Kong Xiaobing, Guo Lei, Liu Yuanyan, Liu Xiangjie. PHOTOVOLTAIC POWER FORECASTING BASED ON MAXIMUM OVERLAP DISCRETE WAVELET TRANSFORM AND DEEP LEARNING[J]. Acta Energiae Solaris Sinica. 2024, 45(5): 576-583 https://doi.org/10.19912/j.0254-0096.tynxb.2022-1993

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