Wind Turbine Power Optimization Based on Extreme Gradient Boosting Model and Periodic Adjustment Strategy

Qin, Yihan; Sun, Zhigang; Ma, Fengli; Ma, Shihao

doi:10.1109/ccdc52312.2021.9601803

Cited by 1 publication

(1 citation statement)

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“…Weights play an important role in XGBoost. Weights are assigned to all the independent variables which are then fed into a decision tree which predicts results [10].…”

Section: Methodsmentioning

confidence: 99%

Predictive Analysis of Wind Power Generationusing Machine Learning

Chauhan,

Kukreti,

Sharma

et al. 2022

IJEAST

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The proposed aim of project predictive analysis of wind power generation using Machine learning is to reduce the cost, provide cleaner and eco friendly source of energy and increase the efficiency of existing systems to greater extent by calculative analysis [1]. As the world is progressing towards renewable and clean energy it is an interdisciplinary research model that is designed to find an optimal way of harnessing wind energy via machine learning models. It is a viable option for Governments and Companies around the world which will help them to set up Wind Energy Plants at appropriate locations for maximizing the output power generation by prediction from the Machine Learning model. The experience obtained while working in the simulation of predictive analysis of wind power generation using machine learning based wind conversion can be successfully implemented in real life scenario helping in increasing renewable energy production. The main objective is to maximize the performance and efficiency of Wind Mills. This project will help in automation-. The control strategy used in this project is for maximizing the performance and efficiency of a Wind Mills.

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“…Weights play an important role in XGBoost. Weights are assigned to all the independent variables which are then fed into a decision tree which predicts results [10].…”

Section: Methodsmentioning

confidence: 99%