Feature Selection and Explainable AI for Transparent Windmill Power Forecasting

Farhana Sultana Eshita (), Tasnim Jahin Mowla () and Abu Bakar Siddique Mahi ()
Additional contact information
Farhana Sultana Eshita: University of Asia Pacific
Tasnim Jahin Mowla: University of Asia Pacific
Abu Bakar Siddique Mahi: University of Asia Pacific

A chapter in Machine Learning Technologies on Energy Economics and Finance, 2025, pp 73-97 from Springer

Abstract: Abstract Integrating wind power into the electrical grid is challenging because wind speeds are unpredictable. Accurate wind power forecasting is crucial for balancing electricity supply and demand. Our research aims to enhance forecasting approaches by employing a correlation-based feature selection technique to identify the most relevant features from the dataset. We evaluate several models, including Extreme Gradient Boosting (XGB), Random Sample Consensus Regressor (RANS), Partial Least Squares Regression (PLSR), Extremely Randomized Trees (ERT), Elastic Net (ENet), Least Absolute Shrinkage and Selection Operator (LASSO), Ridge Regression (RR), and Random Forest Regression (RF). Among these, the RF model exhibits outstanding performance, achieving an R2 score of 99.95%. To improve the interpretability and analysis of feature importance in these machine learning models, we utilize SHAPASH and ELI5 XAI (Explainable Artificial Intelligence) tools. This research highlights the efficacy of the RF model in forecasting wind power generation but also emphasizes the importance of model transparency and interpretability. By leveraging XAI tools, we offer valuable insights into the decision-making processes of these models, identifying the most influential features.

Keywords: Windmill power forecasting; Time series analysis; Machine learning; Feature selection; Explainable AI (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:isochp:978-3-031-95099-5_4

Ordering information: This item can be ordered from
http://www.springer.com/9783031950995

DOI: 10.1007/978-3-031-95099-5_4

Access Statistics for this chapter

More chapters in International Series in Operations Research & Management Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().