Predictive model for PV power generation using RNN (LSTM)

Min Kyeong Park, Jong Man Lee, Won Hee Kang, Jong Min Choi, Kwang Ho Lee

Research output: Contribution to journalArticlepeer-review

29 Citations (Scopus)


In recent years, advanced information technologies, such as deep learning and big data, have been actively applied in building energy management systems to improve energy efficiency. Various studies have been conducted on the prediction of renewable energy performance using machine learning techniques. In this study, a recurrent neural network (RNN) was utilized in predicting photovoltaic (PV) power generation. An RNN is an artificial neural network in which the connection between units is composed of a cyclic structure that can reflect the characteristics of time series. Therefore, to eventually incorporate a model predictive control technique for energy demand and supply matching, this study uses previously measured weather data and PV power generation data to predict the future PV power generation. Various optimization processes, such as normalization, classification of learning data, and setting of layer options, are performed to create a predictive model. Furthermore, 500 hidden neurons and 1 and 3 hidden layers are created and compared. The initial learning rate for both single and multiple-layer options was set to 0.05. As a result, the coefficient of variation of the root mean squared error, Cv(RMSE), which is a statistical index to assess the overall accuracy of the predicted values, was 13.8 % for a single layer and 13.2 % for a multiple-layer long short term memory model. Although no significant difference was observed between the two models, the multiple-layer model showed a slightly reduced error rate than the single-layer model, and the accurate prediction was confirmed to be achievable. Based on this study, a stable energy supply system can be established through linkage with energy storage control by predicting the amount of energy production through renewable energy sources and the amount of energy demand at urban scale.

Original languageEnglish
Pages (from-to)795-803
Number of pages9
JournalJournal of Mechanical Science and Technology
Issue number2
Publication statusPublished - 2021 Feb

Bibliographical note

Funding Information:
This study was partly supported by a Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korean government (MOTIE) (2019271010015B, Development of Eco-friendly Energy Supply Resource Control System). This work was supported by a Korea University Grant (No. K1921431).

Publisher Copyright:
© 2021, The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature.


  • Deep learning
  • Model predictive control
  • PV power generation
  • Recurrent neural network
  • Renewable energy

ASJC Scopus subject areas

  • Mechanics of Materials
  • Mechanical Engineering


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