Improving the calibration of the best member method using quantile regression to forecast extreme temperatures
Temperature influences both the demand and supply of electricity and is therefore a potential cause of blackouts. Like any electricity provider, Electricité de France (EDF) has strong incentives to model the uncertainty in future temperatures using ensemble prediction systems (EPSs). However, the probabilistic representations of the future temperatures provided by EPSs are not reliable enough for electricity generation management. This lack of reliability becomes crucial for extreme temperatures, as these extreme temperatures can result in blackouts. A proven method to solve this problem is the best member method (BMM). This method improves the representation as a whole, but there is still room for improvement in the tails of the distribution. The idea of the BMM is to model the probability distribution of the difference between the forecast and realization. We improve the error modeling in BMM using quantile regression, which is more efficient than the usual two-stage ordinary least squares (OLS) regression. To achieve further improvement, the probability that a given forecast is the best one can be modeled using exogenous variables.