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Hybrid Model Based on Isolated Forest and WT-Catboost for Wind Power Forecasting | IEEE Conference Publication | IEEE Xplore

Hybrid Model Based on Isolated Forest and WT-Catboost for Wind Power Forecasting

Publisher: IEEE

Abstract:

A hybrid model based on isolated forests (IF), wavelet transform (WT), and categorical boosting (CatBoost) is proposed for wind energy prediction. In this prediction meth...View more

Abstract:

A hybrid model based on isolated forests (IF), wavelet transform (WT), and categorical boosting (CatBoost) is proposed for wind energy prediction. In this prediction method, IF is first utilized to catch outliers in the raw wind power time series. Afterward, WT is applied to decompose the wind power sequence processed by outlier detection into several frequencies with more pleasing silhouettes. Finally, CatBoost is employed to learn the nonlinear attributes of these frequencies. The hybrid model is demonstrated on actual wind farm operation data and shows its effectiveness compared with other methods.
Date of Conference: 04-06 November 2022
Date Added to IEEE Xplore: 07 February 2023
ISBN Information:
Publisher: IEEE
Conference Location: Guangzhou, China

Funding Agency:


I. Introduction

In the last few decades, due to global warming and the energy crisis, wind power generation rapidly has expanded globally as a renewable energy source [1]. However, the typical randomness and volatility of wind energy are significant challenges to operation stability and the economic dispatch of modern power systems [2]. An effective solution to lessen the unfavorable influence of these attributes is to apply cutting-edge prediction approaches for wind power [3]. Given the above severe challenges, many engineers and researchers have carried out extensive research on wind power forecasting based on physical, statistical, machine learning, and hybrid models.

References

References is not available for this document.