Evaluation of Probability Distribution Models for Renewable Energy Forecasting Under Uncertain Conditions

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Chinese explanation / 中文解读

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Original abstract

This study explores the capability of several Probability Distribution Functions (PDFs) to effectively model and forecast power generation from renewable energy sources integrated within a Microgrid (MG). The focus is specifically on wind and solar Photovoltaic (PV) systems. The wind speed datasets are analyzed using the Weibull, Rayleigh, Lognormal, Generalized Extreme Value (GEV), and Normal distributions, while solar irradiance data are represented through the Beta, Normal, Triangular, and Lognormal distributions. Actual meteorological data are sourced from the Wind Resource Database (WRDB) and the National Solar Radiation Database (NSRDB), with a focus on the Kauai region of Hawaii. Model performance and suitability are evaluated using multiple statistical indicators, including the coefficient of determination (R²), the Log-Likelihood, the Akaike Information Criterion (AIC), and the Root Mean Square Error (RMSE). The comparative analysis reveals that the Weibull distribution provides the most precise representation of wind speed behavior, outperforming the other models. Meanwhile, for solar irradiance, the Beta distribution demonstrates superior accuracy and adaptability, while the Normal and Triangular models are less effective due to their simplified assumptions.

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