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

Sheshnag Chitlur Sreenivasa; Saurabh Kumar Agarwal; Rajesh Kumar

doi:10.1109/34084POWERI.2014.7117710

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

Sheshnag Chitlur Sreenivasa
, Saurabh Kumar Agarwal
, Rajesh Kumar

Malaviya National Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-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 language	English
Title of host publication	Proceedings of 6th IEEE Power India International Conference, PIICON 2014
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781479960415
DOIs	https://doi.org/10.1109/34084POWERI.2014.7117710
Publication status	Published - 2014
Externally published	Yes
Event	6th IEEE Power India International Conference, PIICON 2014 - Delhi, New Delhi, India Duration: 5 Dec 2014 → 7 Dec 2014

Publication series

Name	Proceedings of 6th IEEE Power India International Conference, PIICON 2014

Conference

Conference	6th IEEE Power India International Conference, PIICON 2014
Country/Territory	India
City	Delhi, New Delhi
Period	5/12/14 → 7/12/14

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

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

Access to Document

10.1109/34084POWERI.2014.7117710

Cite this

Sreenivasa, S. C., Agarwal, S. K., & Kumar, R. (2014). Short term wind forecasting using logistic regression driven hypothesis in artificial neural network. In Proceedings of 6th IEEE Power India International Conference, PIICON 2014 Article 7117710 (Proceedings of 6th IEEE Power India International Conference, PIICON 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/34084POWERI.2014.7117710

Sreenivasa, Sheshnag Chitlur ; Agarwal, Saurabh Kumar ; Kumar, Rajesh. / Short term wind forecasting using logistic regression driven hypothesis in artificial neural network. Proceedings of 6th IEEE Power India International Conference, PIICON 2014. Institute of Electrical and Electronics Engineers Inc., 2014. (Proceedings of 6th IEEE Power India International Conference, PIICON 2014).

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title = "Short term wind forecasting using logistic regression driven hypothesis in artificial neural network",

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.",

keywords = "adaptive neuro fuzzy interface system, artificial neural networks, Fuzzy logic, Time series wind prediction, Wind forecasting",

author = "Sreenivasa, \{Sheshnag Chitlur\} and Agarwal, \{Saurabh Kumar\} and Rajesh Kumar",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 6th IEEE Power India International Conference, PIICON 2014 ; Conference date: 05-12-2014 Through 07-12-2014",

year = "2014",

doi = "10.1109/34084POWERI.2014.7117710",

language = "English",

series = "Proceedings of 6th IEEE Power India International Conference, PIICON 2014",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "Proceedings of 6th IEEE Power India International Conference, PIICON 2014",

address = "United States",

}

Sreenivasa, SC, Agarwal, SK & Kumar, R 2014, Short term wind forecasting using logistic regression driven hypothesis in artificial neural network. in Proceedings of 6th IEEE Power India International Conference, PIICON 2014., 7117710, Proceedings of 6th IEEE Power India International Conference, PIICON 2014, Institute of Electrical and Electronics Engineers Inc., 6th IEEE Power India International Conference, PIICON 2014, Delhi, New Delhi, India, 5/12/14. https://doi.org/10.1109/34084POWERI.2014.7117710

Short term wind forecasting using logistic regression driven hypothesis in artificial neural network. / Sreenivasa, Sheshnag Chitlur; Agarwal, Saurabh Kumar; Kumar, Rajesh.
Proceedings of 6th IEEE Power India International Conference, PIICON 2014. Institute of Electrical and Electronics Engineers Inc., 2014. 7117710 (Proceedings of 6th IEEE Power India International Conference, PIICON 2014).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Short term wind forecasting using logistic regression driven hypothesis in artificial neural network

AU - Sreenivasa, Sheshnag Chitlur

AU - Agarwal, Saurabh Kumar

AU - Kumar, Rajesh

PY - 2014

Y1 - 2014

N2 - 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.

AB - 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.

KW - adaptive neuro fuzzy interface system

KW - artificial neural networks

KW - Fuzzy logic

KW - Time series wind prediction

KW - Wind forecasting

UR - https://www.scopus.com/pages/publications/84938905971

U2 - 10.1109/34084POWERI.2014.7117710

DO - 10.1109/34084POWERI.2014.7117710

M3 - Conference contribution

AN - SCOPUS:84938905971

T3 - Proceedings of 6th IEEE Power India International Conference, PIICON 2014

BT - Proceedings of 6th IEEE Power India International Conference, PIICON 2014

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 6th IEEE Power India International Conference, PIICON 2014

Y2 - 5 December 2014 through 7 December 2014

ER -

Sreenivasa SC, Agarwal SK, Kumar R. Short term wind forecasting using logistic regression driven hypothesis in artificial neural network. In Proceedings of 6th IEEE Power India International Conference, PIICON 2014. Institute of Electrical and Electronics Engineers Inc. 2014. 7117710. (Proceedings of 6th IEEE Power India International Conference, PIICON 2014). doi: 10.1109/34084POWERI.2014.7117710

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

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

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