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

被引：0

作者：

Wang J. ^{[1
]}

Bi L. ^{[1
]}

Zhang K. ^{[1
]}

Sun P. ^{[1
]}

Ma X. ^{[1
]}

机构：

[1] College of Information Engineering, Ningxia University, Yinchuan

来源：

Taiyangneng Xuebao/Acta Energiae Solaris Sinica | 2023年 / 44卷 / 07期

关键词：

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

D O I：

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

中图分类号：

学科分类号：

摘要：

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. © 2023 Science Press. All rights reserved.

引用

页码：168 / 174

页数：6

共 12 条

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