Empirical prediction intervals improve energy forecasting

Abstract

Significance While many forecasters are moving toward generating probabilistic predictions, energy forecasts typically still consist of point projections and scenarios without associated probabilities. Empirical density forecasting methods provide a probabilistic amendment to existing point forecasts. Here we lay the groundwork for evaluating the performance of these methods in the data-scarce setting of long-term forecasts. Results can give policy analysts and other users confidence in estimating forecast uncertainties with empirical methods.