A hybrid model of generalized regression neural network and radial basis function neural network for wind power forecasting in Indian wind farms

References

• J. Varanasi and M. M. Tripathi, “A comparative study of wind power forecasting techniques — A review article,” 2016 3rd International Conference on Computing for Sustainable Global Development (INDIA-Com), New Delhi, 2016, pp. 3649-3655.
   Google Scholar
• Haixiang Zang, Lei Fan, Mian Guo, Zhinong Wei, Guoqiang Sun and LiZhang, “Short-Term Wind Power Interval Forecasting Based on an EEMD-RT-RVM Model” Hindawi Publishing Corporation,, Advances in Meteorology ,Volume 2016 (2016),ArticleID 8760780,10pages.
   Google Scholar
• Alireza Khotanzad, Reza Afkhami-Rohani, Tsun-Liang Lu, Alireza Abaye, Malcolm Davis, and Dominic J. Maratukulam, “ANNSTLF— A Neural-Network-Based Electric Load Forecasting System”, IEEE Transactions On Neural Networks, Vol. 8, No. 4, July 1997.
   PubMedGoogle Scholar
• F.O. Thordarson, H. Madsen, H. A. Nielsen, and P. Pinson, “Conditional weighted combination of wind power forecasts,” Wind Energy, Vol. 13, no. 8, pp. 751–763, Nov. 2010. doi: 10.1002/we.395
   Web of Science ®Google Scholar
• Ignacio J. Ramirez-Rosado, L. Alfredo Fernandez-Jimenez, Cláudio Monteiro, João Sousa, Ricardo Bessa, “Comparison of two new short-term wind-power forecasting systems” Renewable Energy 34 (2009) 1848–1854 doi: 10.1016/j.renene.2008.11.014
   Web of Science ®Google Scholar
• J. P. S. Catalão, H. M. I. Pousinho, and V. M. F. Mendes, “An Artificial Neural Network Approach for Short-Term Wind Power Forecasting in Portugal” Intelligent System Applications to Power Systems, 2009. ISAP ‘09. 15th International Conference
   Google Scholar
• S. A. Pourmousavi Kani, and G. H. Riahy, “A New ANN-Based Methodology for Very Short-Term Wind Speed Prediction Using Markov Chain Approach”, 2008 IEEE Electrical Power & Energy Conference.
   Google Scholar
• Wei-Chang Yeh, Yuan-Ming Yeh, Po-Chun Chang, Yun-Chin Ke, Vera Chung “Forecasting wind power in the Mai Liao Wind Farm based on the multi-layer perceptron artificial neural network model with improved simplified swarm optimization” Electrical Power and Energy Systems 55 (2014) 741–748. doi: 10.1016/j.ijepes.2013.10.001
   Web of Science ®Google Scholar
• Wenyu Zhang, Jie Wu, Jianzhou Wang, Weigang Zhao, Lin Shen, “ Performance analysis of four modified approaches for wind speed forecasting”, Applied Energy 99 (2012) 324–333 doi: 10.1016/j.apenergy.2012.05.029
   Web of Science ®Google Scholar
• Nima Amjady, Farshid Keynia, and Hamidreza Zareipour, “Wind Power Prediction by a New Forecast Engine Composed of Modified Hybrid Neural Network and Enhanced Particle Swarm Optimization” IEEE Transactions On Sustainable Energy, Vol. 2, No. 3, July 2011 doi: 10.1109/TSTE.2011.2114680
   Web of Science ®Google Scholar
• Guglielmo D’Amico, Filippo Petroni, Flavio Prattico “Wind speed and energy forecasting at different time scales A nonparametric approach” Physica A 406 (2014) 59–66 doi: 10.1016/j.physa.2014.03.034
   Web of Science ®Google Scholar
• Hossam Mosbah, Mohamed El-Hawary, “Hourly Electricity Price Forecasting For The Next Month Using Multilayer Neural Network Prédiction Du Tarif Horaire De L’électricité Pour Lemois Suivant Utilisant Les Réseaux De Neurones Multicouches”, Canadian Journal Of Electrical And Computer Engineering, Vol. 39, No. 4, Fall 2016. doi: 10.1109/CJECE.2016.2586939
   Web of Science ®Google Scholar
• Jie Shi, Zhaohao Ding, Wei-Jen Lee, Yongping Yang, Yongqian Liu, Mingming Zhang, “Hybrid Forecasting Model for Very-Short Term Wind Power Forecasting Based on Grey Relational Analysis and Wind Speed Distribution Features”, IEEE Transactions On Smart Grid, Vol. 5, No. 1, January 2014. doi: 10.1109/TSG.2013.2283269
   Web of Science ®Google Scholar
• Wenbin Wu, Mugen Peng, “A Data Mining Approach Combining K-Means Clustering With Bagging Neural Network for Short-Term Wind Power Forecasting” IEEE Internet Of Things Journal, Vol. 4, No. 4, August 2017. doi: 10.1109/JIOT.2017.2677578
   PubMed Web of Science ®Google Scholar
• Jyothi Varanasi, M.M. Tripathi, “Performance Comparison of Generalized Regression Network, Radial Basis Function Network and Support Vector Regression for Wind Power Forecasting”, International Review on Modelling and Simulations (I.R.E.MO.S.),pgs.16-23,Vol.12,N.1, February 2019. DOI: https://doi.org/10.15866/iremos.v12i1.15781
   Google Scholar
• Donald F. Specht,” A General Regression Neural Network”, IEEE Transactions on Neural Networks. Vol. 2 . No. 6. November 1991. doi: 10.1109/72.97934
   PubMedGoogle Scholar
• Wenbin Wu, Mugen Peng, “A Data Mining Approach Combining K-Means Clustering With Bagging Neural Network for Short-Term Wind Power Forecasting”, IEEE Internet of Things Journal, Vol. 4, No. 4, August 2017 doi: 10.1109/JIOT.2017.2677578
   PubMed Web of Science ®Google Scholar
• Can Wan, Jin Lin, Jianhui Wang, Yonghua Song, Zhao Yang Dong, “Direct Quantile Regression for Nonparametric Probabilistic Forecasting of Wind Power Generation” IEEE Transactions On Power Systems, Vol. 32, No. 4, July 2017
   PubMed Web of Science ®Google Scholar