A hybrid system of data-driven approaches for simulating residential energy demand profiles

This paper presents a novel system of data-driven approaches for simulating the dynamics of electricity demand profiles. Demand profiles of individual dwellings are decomposed into deterministic (e.g. ‘Trends’ and ‘Seasonal’) and stochastic (‘remainder’) components using the STL (a Seasonal-Trend decomposition procedure based on Loess) approach. Stochastic components are modelled using a Hidden Markov Model (HMM) and combined with deterministic components to generate synthetic demand profiles. To simulate extreme (peak) demand, the synthetic profiles were post-processed using a Generalised Pareto (GP) distribution, and a percentile-based bias-correction scheme. All the techniques are systematically coupled into a hybrid system, referred to as ‘STL_HMM_GP’. The STL_HMM_GP system is thoroughly accessed and validated by comparing a range of statistical characteristic of observed and simulated profiles for three case study communities. The potentials of the STL_HMM_GP system is demonstrated for simulating aggregated demand profiles, generated using an accessible small sample of observed individual demand profiles.