  Solar forecasting accuracy is affected by weather conditions, and weather
awareness forecasting models are expected to improve the performance. However,
it may not be available and reliable to classify different forecasting tasks by
using only meteorological weather categorization. In this paper, an
unsupervised clustering-based (UC-based) solar forecasting methodology is
developed for short-term (1-hour-ahead) global horizontal irradiance (GHI)
forecasting. This methodology 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
(SVM-PR) is adopted to recognize the category of a certain day using the first
few 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-based methodology is validated by using 1-year of data with six solar
features. Numerical results show that (i) UC-based models outperform non-UC
(all-in-one) models with the same M3 architecture by approximately 20%; (ii)
M3-based models also outperform the single-algorithm machine learning (SAML)
models by approximately 20%.
