Short?Term Deterministic Solar Irradiance Forecasting Considering a Heuristics?Based, Operational Approach

Abstract

Solar energy is an economic and clean power source subject to natural variability, whileenergy storage might attenuate it, ultimately, effective and operationally feasible forecasting techniquesfor energy management are needed for better grid integration. This work presents a noveldeterministic forecast method considering: irradiance pattern classification, Markov chains, fuzzylogic and an operational approach. The method developed was applied in a rolling manner for sixyears to a target location with no prior data to assess performance and its changes as new local databecomes available. Clearness index, diffuse fraction and irradiance hourly forecasts are analyzed ona yearly basis but also for 20 day types, and compared against smart persistence. Results show theproposed method outperforms smart persistence by ~10% for clearness index and diffuse fractionon the base case, but there are significant differences across the 20 day types analyzed, reaching upto +60% for clear days. Forecast lead time has the greatest impact in forecasting performance, whichis important for any practical implementation. Seasonality in data gaps or rejected data can have adefinite effect in performance assessment. A novel, comprehensive and detailed analysis frameworkwas shown to present a better assessment of forecasters’ performance.