Reliable Photovoltaic Generation Forecasting via Quantile Determination

Abstract

Photovoltaic (PV) generation forecasting is critical in advancing the PV penetration level in power systems. However, the inherent uncertainties of PV generation can hardly be quantified by traditional point forecast methods as point forecasting methods can only provide one predicted value at each time step. Due to its superior ability of measuring uncertainties, probabilistic forecasting, which can be in the form of quantile forecasts, is drawing increasing attention. Nevertheless, due to the extensive stochastic nature of PV generation, the existing probabilistic forecasting methods are not sufficiently reliable to quantify the uncertainties of PV generation. This paper adapts quantile determination (QD) to improve the reliability of probabilistic PV generation forecasting. Based on the modified QD, a new method for probabilistic PV generation forecasting is proposed. A one-hour-ahead PV generation forecasting case study reveals that with the proposed QD, the reliability of probabilistic PV forecasting is significantly improved. Moreover, it is also shown that the proposed probabilistic PV generation forecasting method can conduct much more accurate and reliable quantile forecasts, compared with existing methods.