Abstract
The power utilities now a day’s focusing on the use of renewable energy at massive level due to increasing awareness about environment and depleting natural resources. Wind power is one of the main renewable forms of energy but due to the intermittent nature of wind speed, it becomes important to have a precise prediction of speed of wind and wind turbine power before it can be used as primary source of electricity. In this paper, three artificial intelligence methods NARX, NLIO, and RNN Networks are used for short-term wind power forecasting using the data of Kolkata region of India. The simulation results suggest that RNN is able to forecast the wind power better than NARX and NLIO network.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Huang C-M, Kuo C-J, Huang Y-C (2017) Short-term wind power forecasting and uncertainty analysis using a hybrid intelligent method. IET Renew Power Gener 11(5):678–687
Shilpa GN, Dr. Sheshadri GS (2017) Short-term load forecasting using ARIMA model for Karnataka state electrical load. Int J Eng Res Development 13(7):75–79
Elgarhy SM, Othman MM, Taha A, Hasanien HM (2017) Short term load forecasting using ANN technique. In: International Middle East power system conference (MEPCON), Egypt, pp 1385–1394
Buitrago J, Asfour S (2017) Short-term forecasting of electric loads using nonlinear autoregressive artificial neural networks with exogenous vector inputs. In: Energies (MDPI), Miami, pp. 10–40, Jan 2017
Philip OO, Adeleke BT (2017) Predictive and comparative analysis of NARX and NIO timeSeies prediction. Am J Eng Res (AJER). ISSN: 2320-0936
Olaofe ZO, Folly KA (2012) Wind power estimation using recurrent neural network technique. IEEE, July 2012
Kaur H, Salaria DS (2013) Bayesian regularization based neural network tool for software effort estimation. Global J Comput Sci Technol Neural Artif Intell. ISSN: 0975-4350
Bâral A, Căruţaşu G, Botezatu C, Pîrjan A (2016) Comparative analysis between wind and solar forecasting methods using artificial neural networks. In: 16th IEEE international symposium on computational intelligence and informatics (CINTI 2015), Budapest
Kayri M (2016) Predictive abilities of Bayesian regularization and Levenberg–Marquardt algorithms in artificial neural networks: a comparative empirical study on social data. In: Mathematical and computational applications (MDPI), pp 1–11, May 2016
Tripathi MM, Singh SN, Upadhyay KG (2009) Price forecasting in competitive electricity markets: an analysis. In: Proceedings of international conference on energy engineering (ICEE 2009), Puducherry, pp 7–9
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Srivastava, T., Tripathi, M.M. (2020). Predictive Analysis of NARX, NLIO, and RNN Networks for Short-Term Wind Power Forecasting. In: Jain, V., Chaudhary, G., Taplamacioglu, M., Agarwal, M. (eds) Advances in Data Sciences, Security and Applications. Lecture Notes in Electrical Engineering, vol 612. Springer, Singapore. https://doi.org/10.1007/978-981-15-0372-6_4
Download citation
DOI: https://doi.org/10.1007/978-981-15-0372-6_4
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-0371-9
Online ISBN: 978-981-15-0372-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)