<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nahed zemouri</style></author><author><style face="normal" font="default" size="100%">Hassen Bouzgou</style></author><author><style face="normal" font="default" size="100%">Christian A. Gueymard</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Global Solar Radiation Forecasting With Evolutionary Autoregressive Models</style></title><secondary-title><style face="normal" font="default" size="100%">4th International Conference on Artificial Intelligence in Renewable Energetic Systems (IC-AIRES'20)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align:justify&quot;&gt;
	&lt;span style=&quot;text-justify:inter-ideograph&quot;&gt;&lt;span style=&quot;text-autospace:none&quot;&gt;&lt;i&gt;&lt;span lang=&quot;EN-US&quot;&gt;Nowadays, the integration of solar power into the electrical grids is vital to increase energy efficiency and profitability. Effective usage of the instable solar production of photovoltaic (PV) systems necessitates trustworthy forecasting information. Actually, this addition can gives an ameliorated service quality if the solar radiation variation can be forecasted accurately. In this paper, we propose a new forecasting approach that integrates Autoregressive Moving Average (ARMA) and Genetic algorithms (GA) to make benefit of both of them in order to forecast Global Horizontal Irradiance (GHI) component. The proposed approach is compared with the standard ARMA model. The experimental results show that, the proposed approach outperforms the classical ARMA models in terms of mean absolute percentage error (MAPE&lt;/span&gt;&lt;/i&gt;&lt;i&gt;), root mean squared error&lt;/i&gt;&lt;i&gt;&lt;span lang=&quot;EN-US&quot;&gt; (RMSE) &lt;/span&gt;&lt;/i&gt;&lt;i&gt;coefficient of determination&lt;/i&gt;&lt;i&gt;&lt;span lang=&quot;EN-US&quot;&gt; (R)&lt;sup&gt;2 &lt;/sup&gt;and &lt;/span&gt;&lt;/i&gt;&lt;i&gt;the normalized mean squared error &lt;/i&gt;&lt;i&gt;&lt;span lang=&quot;EN-US&quot;&gt;(NMSE).&lt;/span&gt;&lt;/i&gt;&lt;/span&gt;&lt;/span&gt;
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