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Comparison of artificial intelligence and empirical models for energy production estimation of 20 MWp solar photovoltaic plant at the Saharan Medium of Algeria

Kada Bouchouicha (Unité de Recherche en Energies Renouvelables en Milieu Saharien, URERMS, Centre de Développement des Energies Renouvelables, CDER, Adrar, Algeria)
Nadjem Bailek (Department of Matter Sciences, Faculty of Sciences and Technology, Energies and Materials Research Laboratory, Amin El-Akkal Haj Moussa Ag Akhamouk University of Tamanrasset, Tamanrasset, Algeria)
Abdelhak Razagui (Photovoltaic Solar Energy Division, Renewable Energy Development Center CDER, Bouzaréah, Algeria)
Mohamed EL-Shimy (Department of Electrical Power and Machines, Faculty of Engineering, Ain Shams University, Cairo, Egypt)
Mebrouk Bellaoui (Unité de Recherche en Energies Renouvelables en Milieu Saharien, URERMS, Centre de Développement des Energies Renouvelables, CDER, Adrar, Algeria)
Nour El Islam Bachari (Department of Ecology and Environment, Faculty of Biological Science, Houari Boumediene University of Sciences and Technology, Bab Ezzouar, Algeria)

International Journal of Energy Sector Management

ISSN: 1750-6220

Article publication date: 28 July 2020

Issue publication date: 22 January 2021

191

Abstract

Purpose

This study aims to estimate the electric power production of the 20 MWp solar photovoltaic (PV) plant installed in the Adrar region, South of Algeria using minimal knowledge about weather conditions.

Design/methodology/approach

In this study, simulation models based on linear and nonlinear approaches were used to estimate accurate energy production from minimum radiometric and meteorological data. Simulations have been carried out by using multiple linear regression (MLR) and artificial neural network (ANN) models with three basic types of neuron connection architectures, namely, feed-forward neural network, cascade-forward neural network (CNN) and Elman neural network. The performance is measured based on evaluation indexes, namely, mean absolute percentage error, normalized mean absolute error and normalized root mean square error.

Findings

A comparison of the proposed ANN models has been made with MLR models. The performance analysis indicates that all the ANN-based models are superior in prediction accuracy and stability, and among these models, the most accurate results are obtained with the use of CNN-based models.

Practical implications

The considered model will be adopted in solar PV forecasting areas as part of the operational forecasting chain based on numerical weather prediction. It can be an effective and powerful forecasting approach for solar power generation for large-scale PV plants.

Social implications

The operational forecasting system can be used to generate an effective schedule for national grid electricity system operators to ensure the sustainability as well as favourable trading performance in the electricity, such as adjusting the scheduling plan, ensuring power quality, reducing depletion of fossil fuel resources and consequently decreasing the environmental pollution.

Originality/value

The proposed method uses the instantaneous radiometric and meteorological data in 15-min time interval recorded over the two years of operation, which made the result exploits a fact that the energy production estimation of PV power generation station is comparatively more accurate.

Keywords

Acknowledgements

The authors gratefully acknowledge the electricity and renewable energy company SKTM, the SONELGAZ group’s subsidiary for providing meteorological, solar and electrical data sets of the 20 MWp solar PV power plant.

Citation

Bouchouicha, K., Bailek, N., Razagui, A., EL-Shimy, M., Bellaoui, M. and Bachari, N.E.I. (2021), "Comparison of artificial intelligence and empirical models for energy production estimation of 20 MWp solar photovoltaic plant at the Saharan Medium of Algeria", International Journal of Energy Sector Management, Vol. 15 No. 1, pp. 119-138. https://doi.org/10.1108/IJESM-12-2019-0017

Publisher

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Emerald Publishing Limited

Copyright © 2020, Emerald Publishing Limited

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