Probabilistic Forecasting of Daily PV Generation Using Quantile Regression Method

Abstract

Probabilistic PV generation forecasting plays a significant role in the uncertainty management of power systems with higher penetration of PV generation. PV generation forecasting is more challenging due to the stochastic nature of weather conditions. Various outmoded probability models have been espoused for PV generation uncertainty; the most popular ones rely on specific parametric density functions to fit forecasting error. However, PV generation uncertainty has varying probability distribution patterns, and a parametric distribution for forecast error may not always be applicable at different time instants and places. Non-parametric approaches, e.g., quantile regression, on the other hand, estimate the predictive densities directly from the data without any constraints on the distribution shape. On this note, the benefit of the association of a few potential and sensible regressors set with the intricate PV generation pattern is envisioned for effective probabilistic forecasting. The regressors for the proposed quantile regression model are chosen based on the physics of the underlying phenomenon. The effectiveness of the proposed probabilistic forecasting is tested using real-world multi-time instant PV generation data collected from the USA. Out-of-sample quantile forecasts are generated for the PV generation, which is found to be accurate with a minimum deviation of estimated quantiles from the theoretical quantiles. Probability densities are found from these estimated quantiles, and their goodness-of-fit is tested using the famous KS test.