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

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

Research output: Contribution to conferencePaper

1 Citation (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
Publication statusPublished - 1 Dec 2009
Event2009 CIGRE / EEE PES Joint Symposium: Integration of Wide-Scale Renewable Resources into the Power Delivery System - Calgary, AB, Canada
Duration: 29 Jul 200931 Jul 2009

Other

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

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Keywords

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

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Fuel 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. Paper presented at 2009 CIGRE / EEE PES Joint Symposium: Integration of Wide-Scale Renewable Resources into the Power Delivery System, Calgary, AB, Canada.