The Importance of Distance between Photovoltaic Power Stations for Clear Accuracy of Short-Term Photovoltaic Power Forecasting

The current research paper deals with the worldwide problem of photovoltaic (PV) power forecasting by this innovative contribution in short-term PV power forecasting time horizon based on classification methods and nonlinear autoregressive with exogenous input (NARX) neural network model. In the mea...

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Main Authors: Abdelhakim El hendouzi, Abdennaser Bourouhou, Omar Ansari
Format: Article
Language:English
Published: Wiley 2020-01-01
Series:Journal of Electrical and Computer Engineering
Online Access:http://dx.doi.org/10.1155/2020/9586707
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author Abdelhakim El hendouzi
Abdennaser Bourouhou
Omar Ansari
author_facet Abdelhakim El hendouzi
Abdennaser Bourouhou
Omar Ansari
author_sort Abdelhakim El hendouzi
collection DOAJ
description The current research paper deals with the worldwide problem of photovoltaic (PV) power forecasting by this innovative contribution in short-term PV power forecasting time horizon based on classification methods and nonlinear autoregressive with exogenous input (NARX) neural network model. In the meantime, the weather data and PV installation parameters are collected through the data acquisition systems installed beside the three PV systems. At the same time, the PV systems are located in Morocco country, respectively, the 2 kWp PV installation placed at the Higher Normal School of Technical Education (ENSET) in Rabat city, the 3 kWp PV system set at Nouasseur Casablanca city, and the 60 kWp PV installation also based in Rabat city. The multisite modelling approach, meanwhile, is deployed for establishing the flawless short-term PV power forecasting models. As a result, the implementation of different models highlights their achievements in short-term PV power forecasting modelling. Consequently, the comparative study between the benchmarking model and the forecasting methods showed that the forecasting techniques used in this study outperform the smart persistence model not only in terms of normalized root mean square error (nRMSE) and normalized mean absolute error (nMAE) but also in terms of the skill score technique applied to assess the short-term PV power forecasting models.
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institution Kabale University
issn 2090-0147
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record_format Article
series Journal of Electrical and Computer Engineering
spelling doaj-art-0de831719d0b44d4bb02027b0f96bb562025-08-20T03:55:33ZengWileyJournal of Electrical and Computer Engineering2090-01472090-01552020-01-01202010.1155/2020/95867079586707The Importance of Distance between Photovoltaic Power Stations for Clear Accuracy of Short-Term Photovoltaic Power ForecastingAbdelhakim El hendouzi0Abdennaser Bourouhou1Omar Ansari2Lab Research in Electrical Engineering, National School of Computer Science and Systems Analysis and Higher Normal School of Technical Education, Mohammed V University of Rabat, Avenue of the Royal Army, Madinat Al Irfane, District Riad, Rabat 100100, MoroccoLab Research in Electrical Engineering, Higher Normal School of Technical Education, Avenue of the Royal Army, Madinat Al Irfane, District Riad, Rabat 100100, MoroccoLab Research in Mechanical Engineering, Higher Normal School of Technical Education, Avenue of the Royal Army, Madinat Al Irfane, District Riad, Rabat 100100, MoroccoThe current research paper deals with the worldwide problem of photovoltaic (PV) power forecasting by this innovative contribution in short-term PV power forecasting time horizon based on classification methods and nonlinear autoregressive with exogenous input (NARX) neural network model. In the meantime, the weather data and PV installation parameters are collected through the data acquisition systems installed beside the three PV systems. At the same time, the PV systems are located in Morocco country, respectively, the 2 kWp PV installation placed at the Higher Normal School of Technical Education (ENSET) in Rabat city, the 3 kWp PV system set at Nouasseur Casablanca city, and the 60 kWp PV installation also based in Rabat city. The multisite modelling approach, meanwhile, is deployed for establishing the flawless short-term PV power forecasting models. As a result, the implementation of different models highlights their achievements in short-term PV power forecasting modelling. Consequently, the comparative study between the benchmarking model and the forecasting methods showed that the forecasting techniques used in this study outperform the smart persistence model not only in terms of normalized root mean square error (nRMSE) and normalized mean absolute error (nMAE) but also in terms of the skill score technique applied to assess the short-term PV power forecasting models.http://dx.doi.org/10.1155/2020/9586707
spellingShingle Abdelhakim El hendouzi
Abdennaser Bourouhou
Omar Ansari
The Importance of Distance between Photovoltaic Power Stations for Clear Accuracy of Short-Term Photovoltaic Power Forecasting
Journal of Electrical and Computer Engineering
title The Importance of Distance between Photovoltaic Power Stations for Clear Accuracy of Short-Term Photovoltaic Power Forecasting
title_full The Importance of Distance between Photovoltaic Power Stations for Clear Accuracy of Short-Term Photovoltaic Power Forecasting
title_fullStr The Importance of Distance between Photovoltaic Power Stations for Clear Accuracy of Short-Term Photovoltaic Power Forecasting
title_full_unstemmed The Importance of Distance between Photovoltaic Power Stations for Clear Accuracy of Short-Term Photovoltaic Power Forecasting
title_short The Importance of Distance between Photovoltaic Power Stations for Clear Accuracy of Short-Term Photovoltaic Power Forecasting
title_sort importance of distance between photovoltaic power stations for clear accuracy of short term photovoltaic power forecasting
url http://dx.doi.org/10.1155/2020/9586707
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