Intra-hour irradiance forecasting techniques for solar power integration: A review

Yinghao Chu, Mengying Li, Carlos F.M. Coimbra, Daquan Feng, Huaizhi Wang

Research output: Journal article publicationReview articleAcademic researchpeer-review

4 Citations (Scopus)

Abstract

The ever-growing installation of solar power systems imposes severe challenges on the operations of local and regional power grids due to the inherent intermittency and variability of ground-level solar irradiance. In recent decades, solar forecasting methodologies for intra-hour, intra-day and day-ahead energy markets have been extensively explored as cost-effective technologies to mitigate the negative effects on the power grids caused by solar power instability. In this work, the progress in intra-hour solar forecasting methodologies are comprehensively reviewed and concisely summarized. The theories behind the forecasting methodologies and how these theories are applied in various forecasting models are presented. The reviewed mathematical tools include regressive methods, stochastic learning methods, deep learning methods, and genetic algorithm. The reviewed forecasting methodologies include data-driven methods, local-sensing methods, hybrid forecasting methods, and application orientated methods that generate probabilistic forecasts and spatial forecasts. Furthermore, suggestions to accelerate the development of future intra-hour forecasting methods are provided.

Original languageEnglish
Article number103136
JournaliScience
Volume24
Issue number10
DOIs
Publication statusPublished - 22 Oct 2021

Keywords

  • Energy materials
  • Energy resources
  • Energy systems
  • Mechanical engineering

ASJC Scopus subject areas

  • General

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