SHORT-TERM PHOTOVOLTAIC POWER GENERATION PREDICTION BASED ON MULTI-FEATURE FUSION AND XGBOOST-LIGHTGBM-CONVLSTM

Wang Junjie; Bi Li; Zhang Kai; Sun Pengxiang; Ma Xunde

doi:10.19912/j.0254-0096.tynxb.2022-0458

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Acta Energiae Solaris Sinica ›› 2023, Vol. 44 ›› Issue (7) : 168-174. DOI: 10.19912/j.0254-0096.tynxb.2022-0458

SHORT-TERM PHOTOVOLTAIC POWER GENERATION PREDICTION BASED ON MULTI-FEATURE FUSION AND XGBOOST-LIGHTGBM-CONVLSTM

Wang Junjie, Bi Li, Zhang Kai, Sun Pengxiang, Ma Xunde

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Abstract

In this paper, a short-term PV power generation prediction model based on multi-feature fusion and XGBoost-LightGBM-ConvLSTM （XG-LG-CL） is proposed to solve the problems of large prediction error, few feature data and gradient explosion or disappearing of deep neural network model in the traditional single model. Firstly, the relevant factors affecting photovoltaic power generation are analyzed, and the original 11 effective features are increased to 62 effective features by photovoltaic field feature fusion and high-order feature fusion. Secondly, XGBoost, LightGBM and ConvLSTM models are established to extract the temporal and spatial features respectively. Finally, the adaptive weight method is used to mix the three models to predict the power generation. The results show that the prediction accuracy of the model is 88.4% in the experiment of measured data of photovoltaic power generation, which is improved by 3.1-8.6 percentage points compared with the existing prediction method. The model can accurately predict photovoltaic power generation and provide effective data support for the stable operation of power grid.

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photovoltaic power generation / data mining / feature fusion / XGBoost / LightGBM / ConvLSTM

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Wang Junjie, Bi Li, Zhang Kai, Sun Pengxiang, Ma Xunde. SHORT-TERM PHOTOVOLTAIC POWER GENERATION PREDICTION BASED ON MULTI-FEATURE FUSION AND XGBOOST-LIGHTGBM-CONVLSTM[J]. Acta Energiae Solaris Sinica. 2023, 44(7): 168-174 https://doi.org/10.19912/j.0254-0096.tynxb.2022-0458

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