Extracting Trends Ensembles in Solar Irradiance for Green Energy Generation Using Neuro-evolution

Abstract : Globally, there are variations in climate, there is fossil fuel depletion, rising fossil fuel prices, increasing concern regarding energy security, and awareness about the environmental impacts of burning fossil fuels. These factors lead to a growing interest around the world in green and renewable energy resources, solar energy being a common one. A Neuro-evolutionary approach is explored to extract the trend ensembles in the solar irradiance patterns for renewable electric power generation, using the data taken from stations in Al-Ahsa, Kingdom of Saudi Arabia. The algorithm, based on Cartesian Genetic Programming Evolved Artificial Neural Network (CGPANN) was developed and trained for hourly and 24-hourly prediction, using the solar irradiance value as the input parameter. It was tested to predict solar irradiance on hourly, daily, and weekly basis. The proposed technique is 95.48% accurate in solar irradiance prediction.
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Communication dans un congrès
Lazaros Iliadis; Ilias Maglogiannis; Harris Papadopoulos. 10th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2014, Rhodes, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-436, pp.456-465, 2014, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-662-44654-6_45〉
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Mehreen Rehman, Jawad Ali, Gul Khan, Sahibzada Mahmud. Extracting Trends Ensembles in Solar Irradiance for Green Energy Generation Using Neuro-evolution. Lazaros Iliadis; Ilias Maglogiannis; Harris Papadopoulos. 10th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2014, Rhodes, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-436, pp.456-465, 2014, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-662-44654-6_45〉. 〈hal-01391347〉

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