Short-Term Load Forecasting, Profile Identification, and Customer Segmentation: A Methodology Based on Periodic Time Series

摘要

Results from a project in cooperation with the Belgian National Grid Operator ELIA are presented in this paper. Starting from a set of 245 time series, each one corresponding to four years of measurements from a HV-LV substation, individual modeling using Periodic Time Series yields satisfactory results for short-term forecasting or simulation purposes. In addition, we use the station-arity properties of the estimated models to identify typical daily customer profiles. As each one of the 245 substations can be represented by its unique daily profile, it is possible to cluster the 245 profiles in order to obtain a segmentation of the original sample in different classes of customer profiles. This methodology provides a unified framework for the forecasting and clustering problems.