Probabilistic photovoltaic power forecasting model based on deterministic forecasts

Abstract

This paper presents an original probabilistic photovoltaic (PV) power forecasting model for the day-ahead hourly generation in a PV plant. The probabilistic forecasting model is based on 12 deterministic models developed with different techniques. An optimization process, ruled by a genetic algorithm, chooses the forecasts of the deterministic models in order to achieve the probability distribution function (PDF) for the PV generation in each one of the daylight hours of the following day in a parametric approach. The PDFs, which constitute the probabilistic forecasts, are a mixture of normal distributions, each one centred in the forecasts of the selected deterministic models. The genetic algorithm chooses the deterministic forecasts, the variance of the normal distributions and their weights in the mixture. In a case study the proposed model achieves better forecasting results than the obtained with the conditional quantile regression method applied to the same data used to develop the deterministic forecasting models.