Unsupervised Clustering-Based Short-Term Solar Forecasting

Brian Hodge, Cong Feng, Mingjian Cui, Siyuan Lu, Hendrik Hamann, Jie Zhang

Research output: Contribution to journalArticlepeer-review

66 Scopus Citations

Abstract

Solar forecasting accuracy is highly affected by weather conditions, therefore, weather awareness forecasting models are expected to improve the forecasting performance. However, it may not be available or reliable to classify different forecasting tasks by only using predefined meteorological weather categorization. In this paper, an unsupervised clustering-based (UC-based) solar forecasting method is developed for short-term (1-h-ahead) global horizontal irradiance (GHI) forecasting. This UC-based method consists of three parts: GHI time series unsupervised clustering, pattern recognition, and UC-based forecasting. The daily GHI time series is first clustered by an Optimized Cross-validated ClUsteRing (OCCUR) method, which determines the optimal number of clusters and best clustering results. Then, support vector machine pattern recognition is adopted to recognize the category of a certain day using the first four hours' data in the forecasting stage. GHI forecasts are generated by the most suitable models in different clusters, which are built by a two-layer machine learning based multi-model (M3) forecasting framework. The developed UC-M3 method is validated by using 1-year of data with 13 solar features from three information sources. Numerical results show that 1) UC-based models outperform non-UC (all-in-one) models with the same M3 architecture by approximately 20%; and 2) M3-based models also outperform the single-algorithm machine learning models by approximately 20%.

Original languageAmerican English
Article number8536427
Pages (from-to)2174-2185
Number of pages12
JournalIEEE Transactions on Sustainable Energy
Volume10
Issue number4
DOIs
StatePublished - Oct 2019

Bibliographical note

Publisher Copyright:
© 2010-2012 IEEE.

NREL Publication Number

  • NREL/JA-5D00-70678

Keywords

  • machine learning
  • pattern recognition
  • sky imaging
  • Solar forecasting
  • unsupervised clustering

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