View in EDS

Verification of solar irradiance probabilistic forecasts.

Saved in:
Bibliographic Details
Title: Verification of solar irradiance probabilistic forecasts.
Authors: Lauret, Philippe1 (AUTHOR) philippe.lauret@univ-reunion.fr, David, Mathieu1 (AUTHOR) mathieu.david@univ-reunion.fr, Pinson, Pierre2 (AUTHOR) ppin@elektro.dtu.dk
Source: Solar Energy. Dec2019, Vol. 194, p254-271. 18p.
Subjects: Forecasting, Solar energy, Weather forecasting
Geographic Terms: Réunion
Abstract: • A framework for evaluating solar irradiance probabilistic forecasts is proposed. • The verification framework is based on visual diagnostic tools and a set of scores. • The verification metrics are applied to ensemble and quantile forecasts. • It is recommended to use a set of scores to assess the quality of the forecasts. We propose a framework for evaluating the quality of solar irradiance probabilistic forecasts. The verification framework is based on visual diagnostic tools and a set of scoring rules mostly originating from the weather forecast verification community. Two types of probabilistic forecasts are used as a basis to illustrate the application of these verification approaches. The first one consists in ensemble forecasts commonly provided by national or international meteorological centres. The second one originates from statistical methods and produces a set of discrete quantile forecasts, the nominal proportions of which span the unit interval. These probabilistic forecasts are evaluated for two selected sites that experience very different climatic conditions. The first site is located in the continental US while the second one is situated on La Réunion Island. Although visual diagnostic tools can help identify deficiencies in generated forecasts, it is recommended that a set of numerical scores be used to assess the quality of probabilistic forecasts. In particular, the Continuous Ranked Probability Score (CRPS) seems to have all the features needed to evaluate a probabilistic forecasting system and, as such, may become a standard for verifying solar irradiance probabilistic forecasts and by extension probabilistic forecasts of solar power generation. [ABSTRACT FROM AUTHOR]
Copyright of Solar Energy is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Description
Abstract:• A framework for evaluating solar irradiance probabilistic forecasts is proposed. • The verification framework is based on visual diagnostic tools and a set of scores. • The verification metrics are applied to ensemble and quantile forecasts. • It is recommended to use a set of scores to assess the quality of the forecasts. We propose a framework for evaluating the quality of solar irradiance probabilistic forecasts. The verification framework is based on visual diagnostic tools and a set of scoring rules mostly originating from the weather forecast verification community. Two types of probabilistic forecasts are used as a basis to illustrate the application of these verification approaches. The first one consists in ensemble forecasts commonly provided by national or international meteorological centres. The second one originates from statistical methods and produces a set of discrete quantile forecasts, the nominal proportions of which span the unit interval. These probabilistic forecasts are evaluated for two selected sites that experience very different climatic conditions. The first site is located in the continental US while the second one is situated on La Réunion Island. Although visual diagnostic tools can help identify deficiencies in generated forecasts, it is recommended that a set of numerical scores be used to assess the quality of probabilistic forecasts. In particular, the Continuous Ranked Probability Score (CRPS) seems to have all the features needed to evaluate a probabilistic forecasting system and, as such, may become a standard for verifying solar irradiance probabilistic forecasts and by extension probabilistic forecasts of solar power generation. [ABSTRACT FROM AUTHOR]
ISSN:0038092X
DOI:10.1016/j.solener.2019.10.041