文章基本信息

标题：Very-short term solar power generation forecasting based on trend-additive and seasonal-multiplicative smoothing methodology
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作者：Stanislav Eroshenko ; Alexandra Khalyasmaa ; Rustam Valiev 等
期刊名称：E3S Web of Conferences
印刷版ISSN：2267-1242
电子版ISSN：2267-1242
出版年度：2018
卷号：51
页码：2003-2008
DOI：10.1051/e3scconf/20185102003
出版社：EDP Sciences
摘要：In conditions of development of generating facilities on renewable energy sources, the technology runs up to uncertainty in the operational and short-term planning of the power system operating modes. To date, reliable tools for forecasting the generation of solar power stations are required. This paper considers the methodology of operational forecasting of solar power stations output based on the mathematical apparatus of cubic exponential smoothing with trend and seasonal components. The presented methodology was tested based on the measuring data of a real solar power station. The average forecast error was not more than 10% for days with variable clouds and not more than 3% for clear days, which indicates the effectiveness of the proposed approach.