Short term wind forecasting using logistic regression driven hypothesis in artificial neural network

Sheshnag Chitlur Sreenivasa, Saurabh Kumar Agarwal, Rajesh Kumar

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The share of wind power is increasing significantly all over the world. The ever increasing wind power integration poses new issues due to its variability and volatility. Good forecasting techniques are thus important to address these challenges. In this paper, few time series forecasting models like artificial neural networks, adaptive neuro fuzzy interface systems are used for short term prediction of wind speeds and further a new hypothesis for better estimation of wind speed is proposed. The results obtained from a real world case study of a wind farm in the state of Karnataka are presented. In this experimental study, a thorough investigation is carried out, considering the results obtained from the mentioned techniques, the accuracy of the proposed model is found to be better by 13.53% than the existing techniques.

Original languageEnglish
Title of host publicationProceedings of 6th IEEE Power India International Conference, PIICON 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479960415
DOIs
Publication statusPublished - 2014
Externally publishedYes
Event6th IEEE Power India International Conference, PIICON 2014 - Delhi, New Delhi, India
Duration: 5 Dec 20147 Dec 2014

Publication series

NameProceedings of 6th IEEE Power India International Conference, PIICON 2014

Conference

Conference6th IEEE Power India International Conference, PIICON 2014
Country/TerritoryIndia
CityDelhi, New Delhi
Period5/12/147/12/14

Keywords

  • adaptive neuro fuzzy interface system
  • artificial neural networks
  • Fuzzy logic
  • Time series wind prediction
  • Wind forecasting

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Energy Engineering and Power Technology

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