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Prediction of Solar Photovoltaic Power Generation by Weather Using LSTM

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2022, 27(8), pp.23-30
  • DOI : 10.9708/jksci.2022.27.08.023
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : July 4, 2022
  • Accepted : August 9, 2022
  • Published : August 31, 2022

Saem-Mi Lee 1 Kyu-Cheol Cho 1

1인하공업전문대학

Accredited

ABSTRACT

Deep learning analyzes data to discover a series of rules and anticipates the future, helping us in various ways in our lives. For example, prediction of stock prices and agricultural prices. In this research, the results of solar photovoltaic power generation accompanied by weather are analyzed through deep learning in situations where the importance of solar energy use increases, and the amount of power generation is predicted. In this research, we propose a model using LSTM(Long Short Term Memory network) that stand out in time series data prediction. And we compare LSTM’s performance with CNN(Convolutional Neural Network), which is used to analyze various dimensions of data, including images, and CNN-LSTM, which combines the two models. The performance of the three models was compared by calculating the MSE, RMSE, R-Squared with the actual value of the solar photovoltaic power generation performance and the predicted value. As a result, it was found that the performance of the LSTM model was the best. Therefor, this research proposes predicting solar photovoltaic power generation using LSTM.

Citation status

* References for papers published after 2022 are currently being built.