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Predictive Analysis of NARX, NLIO, and RNN Networks for Short-Term Wind Power Forecasting

  • Tushar SrivastavaEmail author
  • M. M. Tripathi
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 612)

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.

Keywords

Bayesian regularization Recurrent neural network Mean absolute percentage error Nonlinear auto-regressive exogenous network Nonlinear input–output 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Delhi Technological UniversityNew DelhiIndia

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