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

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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.
Original languageEnglish
Pages (from-to)277-302
Number of pages26
JournalJournal of Building Performance Simulation
Issue number3
Early online date28 Apr 2021
Publication statusPublished - 4 May 2021


  • Time series decomposition
  • community energy demand
  • energy demand simulation
  • hidden markov model
  • statistical modelling

ASJC Scopus subject areas

  • Architecture
  • Building and Construction
  • Modelling and Simulation
  • Computer Science Applications


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