文章基本信息

标题：Inappropriate machine learning application in real power industry cases
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作者：Alexandra Khalyasmaa ; Pavel Matrenin ; Stanislav Eroshenko 等
期刊名称：International Journal of Electrical and Computer Engineering
电子版ISSN：2088-8708
出版年度：2022
卷号：12
期号：3
页码：3023-3032
DOI：10.11591/ijece.v12i3.pp3023-3032
语种：English
出版社：Institute of Advanced Engineering and Science (IAES)
摘要：Global digital transformation of the energy sector has led to the emergence of multiple digital platform solutions, the implementation of which have revealed new problems associated with continuous growth of data volumes requiring new approaches to their processing and analysis. This article is devoted to the improper application of machine learning approaches and flawed interpretation of their output at various stages of decision support systems development: data collection; model development, training and testing as well as industrial implementation. As a real industrial case study, the article examines the power generation forecasting problem of photovoltaic power plants. The authors supplement the revealed problems with the corresponding recommendation for industrial specialists and software developers.
关键词：digital transformation;intelligent system;machine learning application;power generation forecasting photovoltaic power plants