Open Access Open Access  Restricted Access Subscription or Fee Access

Wind Forecasting Using Various Neural Networks in Machine Learning

Utsav Painuli, Atif Irfan, Vineet Sharma, Anuj Aggarwal, Gargi Mishra


Wind Power Forecasting, as the name applies is a process in which data of past is used to tell what kind of output can be expected from a wind turbine in the foreseeable future. Machine learning, can be said to be a derivation of artificial intelligence that makes it possible for the system to learn automatically and improve upon itself from faults, without needing to tell the system to do it, is used to make wind forecasting better. Artificial Neural Network, are an application of Machine learning and they are able to mimic the decision making abilities of a brain to a certain extent. The various kinds of neural networks that were applied in this paper - Adaptive Neuro Fuzzy Interference System, a type of artificial neural network that takes all of the inputs and all of the outputs of the data into consideration, Multi-Layer Perceptron in which the entire networks has many layers which contain parameters and weights, Nonlinear Autoregressive Exogenous Model, in which there are multiple feedbacks for better relations of the values and Long Short term Memory which can add and remove new info while working.




Full Text:



Jin Zhong; Yunhe Hou; Wu, F.F., “Wind power forecasting and integration to power grids” Green Circuits and Systems (ICGCS), 2010,pp.555-560.

Sideratos G, Hatziargyriou ND. An advanced statistical method for wind power forecasting. IEEE Trans

Rahmani R, Yusof R, Seyedmahmoudian M, Mekhilef S. Hybrid technique of ant colony and particle swarm optimization for short term wind energy forecasting. J. Wind Eng. Ind. Aerodyn.

Najeebullah, Zameer A, Khan A, Javed SJ. Machine learning based short term wind power prediction using a hybrid learning model. Comput. Electr. Eng.

Eldali FA, Hansen TM, Suryanarayanan S, Chong EKP. Employing ARIMA models to improve wind power forecasts: a case study in ERCOT. North Am. Power Symp.

De Alencar DB, deMattos Affonso C, de Oliveira RCL, Rodriguez JLM, Leite JC, Filho JCR. Different models for forecasting wind power generation: case study.

Ekstrom J, Koivisto M, Mellin I, Millar RJ, Lehtonen M. A statistical modeling methodology for long-term wind generation and power ramp simulations in new generation locations.

M. Lydia, S. Suresh Kumar, “A Comprehensive Overview on Wind Power Forecasting”, IPEC 2010,pp.268-273.

Andries P. Engelbrecht, “Computational Intelligence” WILEY publishers, England.

Bhaskar, M.; Jain, A.; Venkata Srinath, N.,” Wind speed forecasting: Present status”, Power System Technology (POWERCON), 2010,pp.1-6.

Hu Q, Zhang S, Yu M, Xie Z. Short-term wind speed or power forecasting with heteroscedastic support vector regression. IEEE T Sustain Energ, 2016; 7: 241- 17 249.

Valdez D., Ortiz V., Cabrera A. and Chairez I “Extended Kalman FilterWeights Adjustment For Neonatal Incubator Neurofuzzy Identification”, 0- 7803-9489-5/06/$20.00/©2006 IEEE.

Di Piazza, Annalisa, Maria Carmela Di Piazza, and Gianpaolo Vitale. "Solar and wind forecasting by NARX neural networks." Renewable Energy and Environmental Sustainability 1 (2016): 39.

Khamis, Azme, and S. N. S. B. Abdullah. "Forecasting wheat price using backpropagation and NARX neural network." The International Journal of Engineering and Science 3.11 (2014): 19-26.

Shen, H-Y., and L-C. Chang. "Online multistep-ahead inundation depth forecasts by recurrent NARX networks." Hydrology and Earth System Sciences 17.3 (2013): 935-945.

Kong, Weicong, et al. "Short-term residential load forecasting based on LSTM recurrent neural network." IEEE Transactions on Smart Grid 10.1 (2017): 841-851.

Zhao, Zheng, et al. "LSTM network: a deep learning approach for short-term traffic forecast." IET Intelligent Transport Systems 11.2 (2017): 68-75.

Siami-Namini, Sima, and Akbar Siami Namin. "Forecasting economics and financial time series: ARIMA vs. LSTM." arXiv preprint arXiv:1803.06386 (2018).


  • There are currently no refbacks.

Copyright (c) 2021 Journal of Telecommunication, Switching Systems and Networks