Wind Speed Forecasting Using Innovative Regression Applications of Machine Learning Techniques

Arash Moradzadeh, Amin Mansour-Saatloo, Morteza Nazari-Heris, Behnam Mohammadi-Ivatloo, Somayeh Asadi

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Scopus citations


In recent years, the development and influence of wind power in the power system have witnessed, which has led to a significant increase in the production and use of wind energy worldwide. Considering the variability of wind velocity, planning, and managing wind intermittency are important parts of wind energy development, so predicting wind speeds for high-efficiency energy production is one of the most important power system planning issues. Nowadays, machine learning methods are widely used to model complex and nonlinear systems such as wind speed or solar radiation. In this chapter, wind speed prediction models using machine learning applications are presented to solve power system planning problems. This study utilized two machine learning applications called multilayer perceptron (MLP) and group method of data handling (GMDH) to predict wind speed. To evaluate the proposed models, the authors will predict the wind speed for 15 months as a short-term wind speed prediction. Wind speed prediction in the 15 months horizon is done hourly for each day. The presented results illustrate the proposed models’ capability and effectiveness for predicting short-term wind speeds based on historical wind speed data and the good correlation between the predicted and actual values of data. Wind speed forecasting and wind resource assessment can show the right investment direction to decision-makers and investors, thereby developing the wind energy industry and creating a sustainable power system.

Original languageEnglish (US)
Title of host publicationPower Systems
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages15
StatePublished - 2021

Publication series

NamePower Systems
ISSN (Print)1612-1287
ISSN (Electronic)1860-4676

All Science Journal Classification (ASJC) codes

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering


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