research-article

Active Learning Model on Wind Turbine Power Generation Based on Polynomial Regression

Authors:

Shengqi Wu,

Tongjun ZhangAuthors Info & Claims

ICDLT '21: Proceedings of the 2021 5th International Conference on Deep Learning Technologies

Pages 111 - 115

https://doi.org/10.1145/3480001.3480002

Published: 12 November 2021 Publication History

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Abstract

Accurately predicting the power generation of wind turbines can help with maintaining the safety and stability of the electric power grid, especially for those grids that rely heavily on wind power. A model based on polynomial regression is constructed to predict the output of a wind turbine under certain environment parameters. A comparison between an active learning model and a random-selection model based on the same regression algorithm is carried out to examine the effectiveness of active learning on reducing the amount of data required for training a model.

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ICDLT '21: Proceedings of the 2021 5th International Conference on Deep Learning Technologies

July 2021

131 pages

ISBN:9781450390163

DOI:10.1145/3480001

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

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Published: 12 November 2021

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ICDLT 2021

ICDLT 2021: 2021 5th International Conference on Deep Learning Technologies

July 23 - 25, 2021

Qingdao, China

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Simple Vertical Axis Wind Turbine for Low Wind Speed

A stochastic DEVS wind turbine component model for wind farm simulation

Cluster-based wind turbine maintenance prioritization for a utility-scale wind farm

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