LOWESS Smoothing and Random Forest Based GRU Model : A Short-term Photovoltaic Power Generation Forecasting Method Energy

Yeming DAI*, Yanxin WANG, Mingming LENG, Xinyu YANG, Qiong ZHOU

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

49 Citations (Scopus)


Accurate prediction of photovoltaic power generation is vital to guarantee smooth operation of power stations and ensure users’ electricity consumption. As a good forecasting tool, Gated Recurrent Unit method has been widely used in different forecasting areas. However, the existing studies ignore the impact of data fluctuations on prediction accuracy, to fill the gaps and enhance prediction accuracy, several different data smoothing techniques are introduced and compared to reduce fluctuations, Random Forest method is used for feature selection, and RepeatVector layer extended by attribute dimensions and TimeDistributed layer with full connectivity are utilized to optimize the Gated Recurrent Unit model. A real-world case from the photovoltaic power plant in Xuhui District, Shanghai, China, is adopted to evaluate the performance of proposed method. The comparing results with Recurrent Neural Networks and Long Short-Term Memory, and the actual data as well, show that the proposed prediction method can effectively improve the prediction accuracy of photovoltaic power generation. We also use the daily and monthly data of The Desert Knowledge Australia Solar Centre in Australia to investigate whether the proposed method is suitable for short-term or medium and long-term predictions. The results indicate that our method is more appropriate for short-term predictions.
Original languageEnglish
Article number124661
Early online date28 Jun 2022
Publication statusPublished - 1 Oct 2022

Bibliographical note

Funding Information:
This work was supported by the National Natural Science Foundation of China [No. 72171126 ], Ministry of Education Project of Humanities and Social Science [No. 20YJA630009 ], Social Science Planning Project of Shandong Province [No. 20CSDJ15 ]. The third author (Mingming Leng) was supported by the Faculty Research Grant of Lingnan University under the grant number DB21B1 .

Publisher Copyright:
© 2022 Elsevier Ltd


  • Photovoltaic Power Generation
  • Prediction
  • Locally Weighted Scatterplot Smoothing
  • Random Forest
  • Gated Recurrent Unit
  • Gated recurrent unit
  • Photovoltaic power generation
  • Locally weighted scatterplot smoothing
  • Random forest


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