ARMA model for short-term forecasting of solar potential: application to a horizontal surface of Dakar site.

OAJ Materials and Devices

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Field Value
 
Title ARMA model for short-term forecasting of solar potential: application to a horizontal surface of Dakar site.
 
Creator MBAYE, AMY
 
Subject Physique-Mathématique
ARMA, Stochastic, forecasting, solar radiation,Dakar.
Predicted Solar Potentiel
 
Description This study focuses on stochastic simulation of solar potential on horizontal surface for short-term forcasting on Dakar site.It aims to optimize the energy production of solar power plants.The inflow of intermittent sources on the senegal National Electricity (SENELEC) electricity distribution network causes difficulties in maintaining to balance between demande and production.However,good planning necessarily needs preliminary needs preliminary evaluation of solar potentiel to control intermittent energy ratio on grid to limit the risks.To achieve this,prediction of the poduction of intermittent energies would be an interesting way.In addition,the stochastic nature of the atmospheric situations that contitute a deterministic factor for the solar potentiel at the surface inspires this recourse to the forcast.Given this situation,SENELEC needs forcasting tools to manage his production.With this in mind,we applied the ARMA model in Dakar site in oder to make a short-term forcast of the solar potentiel.This autoregressive moving average model is based,on the search for optimal adjustment parameters p and q for a better adjustment of the of the considered variable (Radiation).The data used were measured at the higher polytechnic school of Dakar and were collected every hour from October 2016 to September 2017.To evaluate the model, we used the RStudio software.The goal is to implement a reliable forecasting model that would predict more efficiently the capacity of production of SENELEC anytime.The goal is to performance of the method gives a mean root squared RMSE) value of 0.629,R² (correlation coefficient) of 0.963, MAE (mean absolute error) of 0.528 and MBE (mean biais error) of 0.012.These performances show that the model can help photovoltaic solar plant operators and especially SENELEC to better manage their energy production plan.
 
Publisher Collaborating Academics
 
Contributor UCAD-ESP-CIFRES-world laboraty
 
Date 2019-05-30
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier http://caip.co-ac.com/index.php/materialsanddevices/article/view/75
 
Source Materials and Devices; Vol 4, No 1 (2019)
2495-3911
 
Language eng
 
Relation http://caip.co-ac.com/index.php/materialsanddevices/article/view/75/45
 
Rights Copyright (c) 2019 AMY MBAYE
http://creativecommons.org/licenses/by-nc-nd/4.0
 

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