Wind Farm Power Prediction Using a Machine Learning Surrogate Model from a First-Principles Simulation Model

Sebastian E. Pralong, Samuel Martínez-Gutiérrez, Dan E. Kröhling, Alejandro Merino, Gonzalo E. Alvarez, Daniel Sarabia, Ernesto C. Martínez

2025

Abstract

Reliable forecasting of wind farm power generation is essential for ensuring seamless grid integration and optimizing energy management strategies. This paper presents an integrated framework combining a first-principles simulation model of wind turbines as a data source for machine learning techniques to forecast wind farm power output. The simulation model accounts for wind speed, direction, temperature, and other climate variables, and is computationally intensive due to the need to account for the dynamics of each turbine operation, the wake effects, etc. To diminish the computational cost, this work introduces a surrogate Gaussian Processes (GPs) model that approximates the complex simulation model to provide predictions of both the mean and variance of power generation. To forecast future climate conditions, we employ a NARX (Nonlinear Autoregressive with Exogenous Inputs) neural network trained on historical data to account for wind speed, direction, and atmospheric conditions for the next two hours. The NARX model forecasts and the GPs predictions enable fast and accurate real-time forecasting of power generation for the entire wind farm. This approach significantly reduces computational times from hours to seconds while maintaining high accuracy, offering a scalable and efficient solution for real-time wind farm power prediction and online optimization.

Paper Citation

in Harvard Style

Pralong S., Martínez-Gutiérrez S., Kröhling D., Merino A., Alvarez G., Sarabia D. and Martínez E. (2025). Wind Farm Power Prediction Using a Machine Learning Surrogate Model from a First-Principles Simulation Model. In Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO; ISBN 978-989-758-770-2, SciTePress, pages 417-424. DOI: 10.5220/0013709700003982

in Bibtex Style

@conference{icinco25,
author={Sebastian Pralong and Samuel Martínez-Gutiérrez and Dan Kröhling and Alejandro Merino and Gonzalo Alvarez and Daniel Sarabia and Ernesto Martínez},
title={Wind Farm Power Prediction Using a Machine Learning Surrogate Model from a First-Principles Simulation Model},
booktitle={Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO},
year={2025},
pages={417-424},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013709700003982},
isbn={978-989-758-770-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO
TI - Wind Farm Power Prediction Using a Machine Learning Surrogate Model from a First-Principles Simulation Model
SN - 978-989-758-770-2
AU - Pralong S.
AU - Martínez-Gutiérrez S.
AU - Kröhling D.
AU - Merino A.
AU - Alvarez G.
AU - Sarabia D.
AU - Martínez E.
PY - 2025
SP - 417
EP - 424
DO - 10.5220/0013709700003982
PB - SciTePress