PHOTOVOLTAIC POWER PREDICTION BASED ON COMBINED XGBOOST-LSTM MODEL

Tan Haiwang; Yang Qiliang; Xing Jiangchun; Huang Kefeng; Zhao Shuo; Hu Haoyu

doi:10.19912/j.0254-0096.tynxb.2021-0005

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Acta Energiae Solaris Sinica ›› 2022, Vol. 43 ›› Issue (8) : 75-81. DOI: 10.19912/j.0254-0096.tynxb.2021-0005

PHOTOVOLTAIC POWER PREDICTION BASED ON COMBINED XGBOOST-LSTM MODEL

Tan Haiwang¹, Yang Qiliang¹, Xing Jiangchun¹, Huang Kefeng¹, Zhao Shuo², Hu Haoyu¹

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Abstract

A combination model based on XGBoost（eXtreme Gradient Boosting） model and LSTM（Long Short Term Memory） model is proposed in this paper. According to the characteristics of short-term photovoltaic power generation, the XGBoost model and the LSTM model are established respectively in the first place. Then, the XGBoost model is used for preliminary prediction to add features, and the error reciprocal method is used to combine the two models for prediction. Data sets from 2018 big data processing and analysis contest of photovoltaic power station artificial intelligence operation and maintenance are selected for experimental evaluation. The final result shows that the root-mean-square error (RMSE) of the constructed XGBoost-LSTM combination model is 0.214. Compared with the Random Forest, GBDT model, XGBoost model, LSTM model, the proposed method has higher prediction accuracy.

Key words

photovoltaic generation / power prediction / XGBoost model / long short-term memory（LSTM）

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Tan Haiwang, Yang Qiliang, Xing Jiangchun, Huang Kefeng, Zhao Shuo, Hu Haoyu. PHOTOVOLTAIC POWER PREDICTION BASED ON COMBINED XGBOOST-LSTM MODEL[J]. Acta Energiae Solaris Sinica. 2022, 43(8): 75-81 https://doi.org/10.19912/j.0254-0096.tynxb.2021-0005

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