Overview of Wind and Photovoltaic Data Stream Classification and Data Drift Issues

Xinchun Zhu, Yang Wu, Xu Zhao, Yunchen Yang, Shuangquan Liu (), Luyi Shi and Yelong Wu ()
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Xinchun Zhu: Yunnan Electric Power Dispatching and Control Center, Kunming 650011, China
Yang Wu: Yunnan Electric Power Dispatching and Control Center, Kunming 650011, China
Xu Zhao: Yunnan Electric Power Dispatching and Control Center, Kunming 650011, China
Yunchen Yang: Yunnan Electric Power Dispatching and Control Center, Kunming 650011, China
Shuangquan Liu: Yunnan Electric Power Dispatching and Control Center, Kunming 650011, China
Luyi Shi: School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Yelong Wu: China-EU Institute for Clean and Renewable, Huazhong University of Science and Technology, Wuhan 430074, China

Energies, 2024, vol. 17, issue 17, 1-24

Abstract: The development in the fields of clean energy, particularly wind and photovoltaic power, generates a large amount of data streams, and how to mine valuable information from these data to improve the efficiency of power generation has become a hot spot of current research. Traditional classification algorithms cannot cope with dynamically changing data streams, so data stream classification techniques are particularly important. The current data stream classification techniques mainly include decision trees, neural networks, Bayesian networks, and other methods, which have been applied to wind power and photovoltaic power data processing in existing research. However, the data drift problem is gradually highlighted due to the dynamic change in data, which significantly impacts the performance of classification algorithms. This paper reviews the latest research on data stream classification technology in wind power and photovoltaic applications. It provides a detailed introduction to the data drift problem in machine learning, which significantly affects algorithm performance. The discussion covers covariate drift, prior probability drift, and concept drift, analyzing their potential impact on the practical deployment of data stream classification methods in wind and photovoltaic power sectors. Finally, by analyzing examples for addressing data drift in energy-system data stream classification, the article highlights the future prospects of data drift research in this field and suggests areas for improvement. Combined with the systematic knowledge of data stream classification techniques and data drift handling presented, it offers valuable insights for future research.

Keywords: data stream; data drift; wind power; photovoltaics; fault detection (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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