Depending on the amount of data to process, file generation may take longer.

If it takes too long to generate, you can limit the data by, for example, reducing the range of years.

Article

Download BibTeX

Title

Long-term wind power and global warming prediction using MARS, ANN, CART, LR, and RF

Authors

[ 1 ] Instytut Matematyki, Wydział Automatyki, Robotyki i Elektrotechniki, Politechnika Poznańska | [ 2 ] Instytut Inżynierii Bezpieczeństwa i Jakości, Wydział Inżynierii Zarządzania, Politechnika Poznańska | [ P ] employee

Scientific discipline (Law 2.0)

[6.6] Management and quality studies
[7.4] Mathematics

Year of publication

2024

Published in

Journal of Industrial and Management Optimization

Journal year: 2024 | Journal volume: vol. 20 | Journal number: iss. 6

Article type

scientific article

Publication language

english

Keywords
EN
  • wind power prediction
  • MARS regression
  • temperature effects of global warming
  • long term forecasting
Abstract

EN The modeling of electricity generation plays a crucial role in investment and long-term planning in power systems, primarily due to the significant volatility associated with wind and solar energy sources. Nevertheless, forecasting wind speeds for wind turbines based on weather conditions over an extended period is difficult and not feasible. This study provides long-term projections for wind power generation derived from a 2 MW wind turbine for the upcoming year and subsequent years utilizing the Multivariate Adaptive Regression Splines (MARS), Artificial Neural Networks (ANN), Classification And Regression Tree (CART), Linear Regression (LR) and Random Forest (RF) techniques. The research is carried out in two distinct phases. During Phase 1 all considered predictive methods are compared. The research demonstrates that the MARS algorithm is a robust and efficient predictor for wind-based power generation, exhibiting strong competitiveness in its performance. During Phase 2, the MARS algorithm is employed to forecast the future 30-year wind power generation capacity lifespan hourly for nine cities in Texas, USA. It is projected that El Paso and Dallas will witness a mean rise of 8.6% in wind power capacity over three decades, while the remaining seven cities are anticipated to have an average decline of 7.7%. Hence, it is imperative to do a comprehensive and extended evaluation employing the MARS technique compared to ANN, CART, LR and RF before installing a wind turbine. This analysis would serve as a crucial resource for investors, engineers, and researchers involved in decision-making processes on wind turbine projects.

Pages (from - to)

2193 - 2216

DOI

10.3934/jimo.2023162

URL

https://www.aimsciences.org/article/doi/10.3934/jimo.2023162

Ministry points / journal

70

Impact Factor

1,2 [List 2023]

This website uses cookies to remember the authenticated session of the user. For more information, read about Cookies and Privacy Policy.