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A robust multi-location evaluation of a machine learning framework for wind power forecasting.

Usman Ali¹, Muhammad Sufyan¹, Shahzad Ali^1,2

¹Department of Information Sciences, University of Education Lahore, Vehari Campus, Vehari, Pakistan.

|April 30, 2026

Summary

Machine learning algorithms enhance wind power forecasting accuracy. XGBoost and linear-kernel Support Vector Regression (SVR) demonstrate superior performance across diverse datasets, improving wind farm energy production.

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Area of Science:

Renewable Energy
Computational Science

Background:

Accurate wind power prediction is crucial for consistent energy generation from wind farms.
Machine learning (ML) offers advanced solutions for improving wind power forecasting reliability and efficiency.

Purpose of the Study:

To analyze and compare the performance of various ML algorithms for wind power forecasting.
To identify the most effective ML models for diverse geographical wind farm datasets.

Main Methods:

Data preprocessing included outlier elimination using Z-score and IQR methods.
Three ML algorithms (XGBoost, Random Forest Regressor (RFR), and Support Vector Regression (SVR)) with different kernels (RBF, polynomial, linear) were trained and evaluated.
Performance was assessed using R2 and Mean Absolute Error (MAE) metrics.

Main Results:

XGBoost achieved R2 values of 0.99 across all locations with MAE ranging from 11.10 to 15.94.
Linear-kernel SVR also attained R2 values of 0.99 with low MAE on all datasets.
RFR showed strong performance but had a significantly lower R2 (0.83) and higher MAE (600.81) at one site.

Conclusions:

XGBoost and linear-kernel SVR are highly effective for wind power forecasting on diverse datasets.
These models provide high accuracy and low error rates, essential for optimizing wind farm energy production.