An artificial Neural Network model for wind energy estimation

Research output: Contribution to book or proceedingConference articlepeer-review

6 Scopus citations

Abstract

Wind energy resources are ideally suited for distributed generation systems to provide electricity for residential use. This paper proposes a novel method for wind energy estimation in the state of Georgia. This method is based on Artificial Neural Network (ANN) using real data obtained from several weather station sites around the state the proposed ANN model was trained and then tested using a local station located in Savannah the ANN inputs are elevation, latitude, longitude, day, temperatures (min/max), and the output is the daily wind speed the model was efficiently implemented in Simulink environment using closed-form algebraic equations which eliminated the need for repeated training the ANN model was formulated with suitable numbers of layers/neurons which was trained and tested with excellent regression constant. Furthermore, the ANN model has the ability to interpolate between learning curves to generate wind speed estimates for different locations. It is anticipated that this model will be able to successfully select sites for wind turbine installations for residential applications in the state of Georgia.

Original languageEnglish
Title of host publicationIEEE SoutheastCon 2015 - Conference Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
EditionJune
ISBN (Electronic)9781467373005
DOIs
StatePublished - Jun 24 2015
EventIEEE SoutheastCon 2015 - Fort Lauderdale, United States
Duration: Apr 9 2015Apr 12 2015

Publication series

NameConference Proceedings - IEEE SOUTHEASTCON
NumberJune
Volume2015-June
ISSN (Print)1091-0050
ISSN (Electronic)1558-058X

Conference

ConferenceIEEE SoutheastCon 2015
Country/TerritoryUnited States
CityFort Lauderdale
Period04/9/1504/12/15

Scopus Subject Areas

  • Computer Networks and Communications
  • Software
  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Signal Processing

Keywords

  • Estimation
  • Neural Network
  • Renewable
  • Wind Energy
  • Wind Prediction

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