A Compound Wind Power Forecasting Strategy Based on Clustering, Two-Stage Decomposition, Parameter Optimization, and Optimal Combination of Multiple Machine Learning Approaches

A Compound Wind Power Forecasting Strategy Based on Clustering, Two-Stage Decomposition, Parameter Optimization, and Optimal Combination of Multiple Machine Learning Approaches

Given the large-scale exploitation and utilization of wind power, the problems caused by the high stochastic and random characteristics of wind speed make researchers develop more reliable and precise wind power forecasting (WPF) models. To obtain better predicting accuracy, this study proposes a no...

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Bibliographic Details
Main Authors: Sizhou Sun, Jingqi Fu, Ang Li
Format: Article
Language:English
Published: MDPI AG 2019-09-01
Series:Energies
Subjects:
two-stage decomposition
extreme learning machine
least square support vector machine
wavelet neural network
partial autocorrelation function
wind power forecasting
Online Access:https://www.mdpi.com/1996-1073/12/18/3586

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https://www.mdpi.com/1996-1073/12/18/3586

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