An Hour-Ahead PV Power Forecasting Method Based on an RNN-LSTM Model for Three Different PV Plants

Muhammad Naveed Akhtar*, Saad Mekhilef, Hazlie Mokhlis, Ziyad M. Almohaimeed, Munir Azam Muhammad, Anis Salwa Mohd Khairuddin, Rizwan Akram, Muhammad Hussain*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

50 Citations (Scopus)
92 Downloads (Pure)


Incorporating solar energy into a grid necessitates an accurate power production forecast for photovoltaic (PV) facilities. In this research, output PV power was predicted at an hour ahead on yearly basis for three different PV plants based on polycrystalline (p-si), monocrystalline (m-si), and thin-film (a-si) technologies over a four-year period. Wind speed, module temperature, ambiance, and solar irradiation were among the input characteristics taken into account. Each PV plant power output was the output parameter. A deep learning method (RNN-LSTM) was developed and evaluated against existing techniques to forecast the PV output power of the selected PV plant. The proposed technique was compared with regression (GPR, GPR (PCA)), hybrid ANFIS (grid partitioning, subtractive clustering and FCM) and machine learning (ANN, SVR, SVR (PCA)) methods. Furthermore, different LSTM structures were also investigated, with recurrent neural networks (RNN) based on 2019 data to determine the best structure. The following parameters of prediction accuracy measure were considered: RMSE, MSE, MAE, correlation (r) and determination (R2) coefficients. In comparison to all other approaches, RNN-LSTM had higher prediction accuracy on the basis of minimum (RMSE and MSE) and maximum (r and R2). The p-si, m-si and a-si PV plants showed the lowest RMSE values of 26.85 W/m2, 19.78 W/m2 and 39.2 W/m2 respectively. Moreover, the proposed method was found to be robust and flexible in forecasting the output power of the three considered different photovoltaic plants.

Original languageEnglish
Article number2243
Issue number6
Publication statusPublished - 18 Mar 2022


  • Hour-ahead prediction
  • PV power forecasting
  • Deep learning

ASJC Scopus subject areas

  • Control and Optimization
  • Energy (miscellaneous)
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Fuel Technology
  • Renewable Energy, Sustainability and the Environment


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