ULTRA-SHORT-TERM PHOTOVOLTAIC POWER FORECASTING BASED ON WAVELET PACKET TRANSFORM AND DEEP LEARNING

Liu Yuanyan; Kong Xiaobing; Ma Lele; Liu Xiangjie

doi:10.19912/j.0254-0096.tynxb.2023-0033

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Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (5) : 537-546. DOI: 10.19912/j.0254-0096.tynxb.2023-0033

ULTRA-SHORT-TERM PHOTOVOLTAIC POWER FORECASTING BASED ON WAVELET PACKET TRANSFORM AND DEEP LEARNING

Liu Yuanyan, Kong Xiaobing, Ma Lele, Liu Xiangjie

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Abstract

Considering the highly varying and complex features of photovoltaic power generation, this paper constitutes a hybrid Photovoltaic （PV） power forecasting （PVPF） method based on gated recurrent unit （GRU） combining with wavelet packet transform （WPT） algorithm. First, correlation analysis is used to select the main meteorological factors while wavelet packet decomposition is used to decompose the original PV power into a series of sub-signals. A similar day selection method based on levy-flight BAS algorithm is proposed to select historical days similar to the forecast day from the real-time massive data. Deep learning model for PVPF is established using a group of GRU networks. These GRU forecasting sub-signals are synthesized to form the final forecasting PV power. The simulation results verify that the proposed method shows obvious advantages in terms of both forecasting accuracy and computational efficiency.

Key words

PV power / power forecasting / wavelet packet transform / similar day / gated recurrent unit / beetle antennae search algorithm

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Liu Yuanyan, Kong Xiaobing, Ma Lele, Liu Xiangjie. ULTRA-SHORT-TERM PHOTOVOLTAIC POWER FORECASTING BASED ON WAVELET PACKET TRANSFORM AND DEEP LEARNING[J]. Acta Energiae Solaris Sinica. 2024, 45(5): 537-546 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0033

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