Modeling climate parameters for renewable energy applications in the UAE using neural networks

L. El Chaar, L. A. Lamont, Mansour Karkoub

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

This paper aims to create prediction models for both global solar radiation and wind speed for the city of Abu Dhabi in the United Arab Emirates. To do so neural network techniques using feed-forward back propagation were deployed and samples for the month of January for such models are presented. The results confirm the accuracy of the models and compare the measured output with the neural network trained outputs. Such models will then be used for estimating power generation using photovoltaics and/or wind turbines.

Original languageEnglish
Title of host publication2009 CIGRE/IEEE PES Joint Symposium Integration of Wide-Scale Renewable Resources into the Power Delivery System, CIGRE/PES 2009
Publication statusPublished - 2009
Externally publishedYes
Event2009 CIGRE/IEEE PES Joint Symposium Integration of Wide-Scale Renewable Resources into the Power Delivery System, CIGRE/PES 2009 - Calgary, AB, Canada
Duration: 29 Jul 200931 Jul 2009

Other

Other2009 CIGRE/IEEE PES Joint Symposium Integration of Wide-Scale Renewable Resources into the Power Delivery System, CIGRE/PES 2009
CountryCanada
CityCalgary, AB
Period29/7/0931/7/09

Fingerprint

Neural networks
Solar wind
Solar radiation
Backpropagation
Wind turbines
Power generation

Keywords

  • Empirical models
  • Feedforward neural network
  • Meteorological
  • Neural networks
  • Prediction methods
  • Solar energy
  • Solar radiation
  • Wind energy

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Renewable Energy, Sustainability and the Environment

Cite this

El Chaar, L., Lamont, L. A., & Karkoub, M. (2009). Modeling climate parameters for renewable energy applications in the UAE using neural networks. In 2009 CIGRE/IEEE PES Joint Symposium Integration of Wide-Scale Renewable Resources into the Power Delivery System, CIGRE/PES 2009 [5211184]

Modeling climate parameters for renewable energy applications in the UAE using neural networks. / El Chaar, L.; Lamont, L. A.; Karkoub, Mansour.

2009 CIGRE/IEEE PES Joint Symposium Integration of Wide-Scale Renewable Resources into the Power Delivery System, CIGRE/PES 2009. 2009. 5211184.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

El Chaar, L, Lamont, LA & Karkoub, M 2009, Modeling climate parameters for renewable energy applications in the UAE using neural networks. in 2009 CIGRE/IEEE PES Joint Symposium Integration of Wide-Scale Renewable Resources into the Power Delivery System, CIGRE/PES 2009., 5211184, 2009 CIGRE/IEEE PES Joint Symposium Integration of Wide-Scale Renewable Resources into the Power Delivery System, CIGRE/PES 2009, Calgary, AB, Canada, 29/7/09.
El Chaar L, Lamont LA, Karkoub M. Modeling climate parameters for renewable energy applications in the UAE using neural networks. In 2009 CIGRE/IEEE PES Joint Symposium Integration of Wide-Scale Renewable Resources into the Power Delivery System, CIGRE/PES 2009. 2009. 5211184
El Chaar, L. ; Lamont, L. A. ; Karkoub, Mansour. / Modeling climate parameters for renewable energy applications in the UAE using neural networks. 2009 CIGRE/IEEE PES Joint Symposium Integration of Wide-Scale Renewable Resources into the Power Delivery System, CIGRE/PES 2009. 2009.
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