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

Muhammad Akhter, Saad Mekhilef, Hazlie Mokhlis , Ziyad M. Almohaimeed, Munir Azam Muhammad, Anis Salwa Mohd Khairuddin, Rizwan Akram, Muhammad Majid Hussain

Allbwn ymchwil: Cyfraniad at gyfnodolynErthygladolygiad gan gymheiriaid

2 Wedi eu Llwytho i Lawr (Pure)

Crynodeb

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.
Iaith wreiddiolSaesneg
Rhif yr erthygl2243
Nifer y tudalennau21
CyfnodolynEnergies
Cyfrol15
Rhif cyhoeddi6
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 18 Maw 2022

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