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Homepage>IEC Standards>IEC TR 63043:2020 - Renewable energy power forecasting technology
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download between 0-24 hoursReleased: 2020-11-27
IEC TR 63043:2020 - Renewable energy power forecasting technology

IEC TR 63043:2020

Renewable energy power forecasting technology

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Standard number:IEC TR 63043:2020
Released:2020-11-27
Language:English
DESCRIPTION

IEC TR 63043:2020

IEC TR 63043:2020(E), which is a technical report, describes common practices and state of the art for renewable energy power forecasting technology, including general data demands, renewable energy power forecasting methods and forecasting error evaluation. For the purposes of this document, renewable energy refers to variable renewable energy, which mainly comprises wind power and photovoltaic (PV) power – these are the focus of the document. Other variable renewable energies, like concentrating solar power, wave power and tidal power, etc., are not presented in this document, since their capacity is small, while hydro power forecasting is a significantly different field, and so not covered here. The objects of renewable energy power forecasting can be wind turbines, or a wind farm, or a region with lots of wind farms (respectively PV systems, PV power stations and regions with high PV penetration). This document focuses on providing technical guidance concerning forecasting technologies of multiple spatial and temporal scales, probabilistic forecasting, and ramp event forecasting for wind power and PV power. This document outlines the basic aspects of renewable energy power forecasting technology. This is the first IEC document related to renewable energy power forecasting. The contents of this document will find an application in the following potential areas: • support the development and future research for renewable energy power forecasting technology, by showing current state of the art; • evaluation of the forecasting performance during the design and operation of renewable energy power forecasting system; • provide information for benchmarking renewable forecasting technologies, including methods used, data required and evaluation techniques

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