Extracting Trends Ensembles in Solar Irradiance for Green Energy Generation Using Neuro-evolution - Artificial Intelligence Applications and Innovations (AIAI 2014) Access content directly
Conference Papers Year : 2014

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.
Fichier principal
Vignette du fichier
978-3-662-44654-6_45_Chapter.pdf (4.85 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01391347 , version 1 (03-11-2016)

Licence

Attribution

Identifiers

Cite

Mehreen Rehman, Jawad Ali, Gul Muhammad Khan, Sahibzada Ali Mahmud. Extracting Trends Ensembles in Solar Irradiance for Green Energy Generation Using Neuro-evolution. 10th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2014, Rhodes, Greece. pp.456-465, ⟨10.1007/978-3-662-44654-6_45⟩. ⟨hal-01391347⟩
164 View
89 Download

Altmetric

Share

Gmail Facebook X LinkedIn More