Prediction Intervals for Electric Load Forecast: Evaluation for Different Profiles

Abstract

Electricity industries throughout the world have been using load profiles for many years. Electrical load data contain valuable information that can be useful for both electricity producers and consumers. Load forecasting is a fundamental and important task to operate power systems efficiently and economically. Currently, prediction intervals (PIs) are assuming increasing importance comparatively to point forecast that cannot properly handle forecast uncertainties, since they are capable to compromise informativeness and correctness. This paper aims to demonstrate that different demand profiles clearly influence PIs reliability and width. The evaluation is performed using data from different customers on the basis of their electricity behavior using hierarchical clustering, and taking the Kullback-Leibler divergence as the distance metric. PIs are obtained using two different strategies: (1) dual perturb and combine algorithm and (2) conformal prediction. It was possible to demonstrate that different demand profiles clearly influence PI reliability and width for both models. The knowledge retrieved from the analysis of the load patterns is useful and can be used to support the selection of the best method to interval forecast, considering a specific location. And also, it can support the selection of an optimum confidence level, considering that a too wide PI conveys little information and is of no use for decision making.