Title :
Residential Electrical Load Model Based on Mixture Model Clustering and Markov Models
Author :
Labeeuw, Wouter ; Deconinck, Geert
Author_Institution :
Katholieke Univ. Leuven, Leuven, Belgium
Abstract :
Detailed large-scale simulations require a lot of data. Residential electrical load profiles are well protected by privacy laws. Representative residential electrical load generators get around the privacy problem and allow for Monte Carlo simulations. A top-down model of the residential electrical load, based on a dataset of over 1300 load profiles, is presented in this paper. The load profiles are clustered by a Mixed Model to group similar ones. Within the group, a behavior model is constructed with a Markov model. The states of the Markov models are based on the probability distribution of the electrical power. A second Markov model is created to randomize the behavior. A load profile is created by first performing a random-walking of the Markov models to get a sequence of states. The inverse of the probability distribution of the electrical power is used to translate the resulting states into electrical power.
Keywords :
Markov processes; Monte Carlo methods; load forecasting; load management; Markov models; Monte Carlo simulations; electrical power; large-scale simulations; mixture model clustering; privacy laws; privacy problem; probability distribution; random walking; residential electrical load generators; Correlation; Electricity; Hidden Markov models; Joints; Load modeling; Markov processes; Mathematical model; Clustering; Markov models; data analysis; statistical distributions;
Journal_Title :
Industrial Informatics, IEEE Transactions on
DOI :
10.1109/TII.2013.2240309