Time-series decomposition of power demand data to extract uncertain features

Imanishi, Tomoya; Yoshida, Masahiro; Wijekoon, Janaka; Nishi, Hiroaki

doi:10.1109/isie.2017.8001473

Cited by 3 publications

(1 citation statement)

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“…Few studies recently investigated the potentials of timeseries decomposition techniques in optimising the model performance, some of the related examples are briefly discussed here. In (Imanishi et al 2017), the time-series decomposition approach is applied to the power demand information, which is collected from multiple houses in Japan for a month and at a temporal resolution of 30-minutes. This study investigated the relationships between decomposed components and customer information to evaluate whether the approach is meaningful for feature extraction.…”

Section: Application Of Time-series Decomposition Approach In Energy Demand Modellingmentioning

confidence: 99%

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

Patidar

Jenkins

Peacock

et al. 2021

Journal of Building Performance Simulation

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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.

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Section: Application Of Time-series Decomposition Approach In Energy Demand Modellingmentioning

confidence: 99%