Spatially transferable machine learning wind power prediction models: v−logit random forests

Mario Arrieta-Prieto and Kristen R. Schell

Renewable Energy, 2024, vol. 223, issue C

Abstract: Wind power prediction models provide essential information to wind farm developers and power system operators on the power available at an undeveloped location. Traditionally, statistical models require recalibration of the model’s parameters in order to fit the model to a specific location’s dynamics. To mitigate this computational expense, this research develops a data-driven wind power prediction model that is spatially transferable, without the need for recalibration of the model’s parameters at a new location. This study also develops a wind direction prediction and interpolation model, as well as a transferability metric that demonstrates the model’s predictive accuracy at a new location. The transferability metric is evaluated in an exhaustive experimental setting. Results show that a v-logit random forest with minimum information requirements is the most transferable random forest variant for wind power prediction, with an error rate as low as 9% and never more than 20% when a transferred model is applied to a new location. The case study illustrates that the transferred model outperforms a site-specific, in-situ model for several existing wind farms. Clustering based on similarity, validated by cophenetic correlation and mapping, reveal that both the latitude of the farms, and the similarity of their turbine layouts, increase model transferability.

Keywords: v-logit; Random forest; Hierarchical clustering; Wind direction prediction; Wind power prediction; Ripley’s K function (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960148124001319
Full text for ScienceDirect subscribers only

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:eee:renene:v:223:y:2024:i:c:s0960148124001319

DOI: 10.1016/j.renene.2024.120066

Access Statistics for this article

Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides

More articles in Renewable Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().