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

Muhammad Naveed Akhter, Saad Mekhilef, Hazlie Mokhlis, Ziyad M. Almohaimeed, Munir Azam Muhammad, Anis Salwa Mohd Khairuddin, Rizwan Akram and Muhammad Majid Hussain
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Muhammad Naveed Akhter: Department of Electrical Engineering, Rachna College of Engineering and Technology, University of Engineering and Technology Lahore, Gujranwala 52250, Pakistan
Saad Mekhilef: Power Electronics and Renewable Energy Research Laboratory (PEARL), Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia
Hazlie Mokhlis: Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia
Ziyad M. Almohaimeed: Department of Electrical Engineering, College of Engineering, Qassim University, Buraidah 51452, Saudi Arabia
Munir Azam Muhammad: Department of Electrical Engineering, Main Campus, Iqra University, Karachi 75500, Pakistan
Anis Salwa Mohd Khairuddin: Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia
Rizwan Akram: Department of Electrical Engineering, College of Engineering, Qassim University, Buraidah 51452, Saudi Arabia
Muhammad Majid Hussain: Department of Electrical and Electronic Engineering, University of South Wales, Pontypirdd CF37 1DL, UK

Energies, 2022, vol. 15, issue 6, 1-21

Abstract: 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 ( R 2 ) 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 R 2 ). The p-si, m-si and a-si PV plants showed the lowest RMSE values of 26.85 W/m 2 , 19.78 W/m 2 and 39.2 W/m 2 respectively. Moreover, the proposed method was found to be robust and flexible in forecasting the output power of the three considered different photovoltaic plants.

Keywords: hour-ahead prediction; PV power forecasting; RNN-LSTM; deep learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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