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Solar forecasting accuracy is affected by weather conditions, and weatherawareness forecasting models are expected to improve the performance. However,it may not be available and reliable to classify different forecasting tasks byusing only meteorological weather categorization. In this paper, anunsupervised clustering-based (UC-based) solar forecasting methodology isdeveloped for short-term (1-hour-ahead) global horizontal irradiance (GHI)forecasting. This methodology consists of three parts: GHI time seriesunsupervised clustering, pattern recognition, and UC-based forecasting. Thedaily GHI time series is first clustered by an Optimized Cross-validatedClUsteRing (OCCUR) method, which determines the optimal number of clusters andbest clustering results. Then, support vector machine pattern recognition(SVM-PR) is adopted to recognize the category of a certain day using the firstfew hours' data in the forecasting stage. GHI forecasts are generated by themost suitable models in different clusters, which are built by a two-layerMachine learning based Multi-Model (M3) forecasting framework. The developedUC-based methodology is validated by using 1-year of data with six solarfeatures. 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%.

An Unsupervised Clustering-Based Short-Term Solar Forecasting Methodology Using Multi-Model Machine Learning Blending