Multistep-ahead solar radiation forecasting scheme based on the light gradient boosting machine: A case study of Jeju Island

Jinwoong Park, Jihoon Moon, Seungmin Jung, Eenjun Hwang

Research output: Contribution to journalArticle

Abstract

Smart islands have focused on renewable energy sources, such as solar and wind, to achieve energy self-sufficiency. Because solar photovoltaic (PV) power has the advantage of less noise and easier installation than wind power, it is more flexible in selecting a location for installation. A PV power system can be operated more efficiently by predicting the amount of global solar radiation for solar power generation. Thus far, most studies have addressed day-ahead probabilistic forecasting to predict global solar radiation. However, day-ahead probabilistic forecasting has limitations in responding quickly to sudden changes in the external environment. Although multistep-ahead (MSA) forecasting can be used for this purpose, traditional machine learning models are unsuitable because of the substantial training time. In this paper, we propose an accurate MSA global solar radiation forecasting model based on the light gradient boosting machine (LightGBM), which can handle the training-time problem and provide higher prediction performance compared to other boosting methods. To demonstrate the validity of the proposed model, we conducted a global solar radiation prediction for two regions on Jeju Island, the largest island in South Korea. The experiment results demonstrated that the proposed model can achieve better predictive performance than the tree-based ensemble and deep learning methods.

Original languageEnglish
Article number2271
JournalRemote Sensing
Volume12
Issue number14
DOIs
Publication statusPublished - 2020 Jul 1

Keywords

  • Feature importance
  • Light gradient boosting machine
  • Multistep-ahead prediction
  • Smart island
  • Solar energy
  • Solar radiation forecasting

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)

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