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A Survey on ML Models of Wind Energy Forecasting


Affiliations
1 St. Xavier’s Catholic College of Engineering., India
2 St. Xavier’s Catholic College of Engineering, India
     

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Given the intermittent nature of the wind, accurate wind energy forecasting is significant to the proper utilization of renewable energy sources. Various types of data processing methods are successfully applied to assist these models and further improve forecasting performance. Comprehensive research of their methodologies is called on for a thorough understanding of current challenges that affect model accuracy and efficiency. An overall analysis of their intentions, positions, characteristics, and implementation details is provided. A general evaluation is carried out from different perspectives including accuracy improvement, usage frequency, consuming time, robustness to parameters, maturity, and implementation difficulty. Among the existing data processing methods, outlier detection and filter-based correction are relatively less used. Their potential can be better explored in the future. Furthermore, some possible research directions and challenges of data processing in wind energy forecasting are provided, in order to encourage subsequent research.

Keywords

ARMA Model, Artificial Neural Networks, Forecasting, Slap Swarm
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  • A Survey on ML Models of Wind Energy Forecasting

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Authors

T. M. Angelin Monisha Sharean
St. Xavier’s Catholic College of Engineering., India
S Sanjana
St. Xavier’s Catholic College of Engineering, India

Abstract


Given the intermittent nature of the wind, accurate wind energy forecasting is significant to the proper utilization of renewable energy sources. Various types of data processing methods are successfully applied to assist these models and further improve forecasting performance. Comprehensive research of their methodologies is called on for a thorough understanding of current challenges that affect model accuracy and efficiency. An overall analysis of their intentions, positions, characteristics, and implementation details is provided. A general evaluation is carried out from different perspectives including accuracy improvement, usage frequency, consuming time, robustness to parameters, maturity, and implementation difficulty. Among the existing data processing methods, outlier detection and filter-based correction are relatively less used. Their potential can be better explored in the future. Furthermore, some possible research directions and challenges of data processing in wind energy forecasting are provided, in order to encourage subsequent research.

Keywords


ARMA Model, Artificial Neural Networks, Forecasting, Slap Swarm

References