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
N1 - Publisher Copyright:
© 2014 IEEE.
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 - http://www.scopus.com/inward/record.url?scp=84938905971&partnerID=8YFLogxK
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 -