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

Abstract

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.

Associating Climatic Trends with Stochastic Modelling of Flow Sequences

“…Thus, the HMM_GP framework is shown to effectively simulate the dynamics of the climatic variable along with river runoff. In fact, in earlier work by the authors, the proposed modelling schematic was shown to generate statistical synthetics time series for a range of applications including energy demand series at different resolution in the range of 5-30 min [58] and for Scottish rivers inflow sequences (15 min inflow) [17]. In the present work, the model is first applied to simulate the statistical dynamics of climatic variables, then a novel climate module is developed.…”

“…Further details on snowmelt runoff for the Beas basin can be found elsewhere [57]. The methodological framework of the HMM_GP model consists of four stages and is illustrated in Figure 2, as adapted from [58]. The colour schematics applied in Figure 2 represent the key four stages of the HMM_GP modelling framework which are briefly described below and can found elsewhere [58] for underlying technical details.…”

“…The methodological framework of the HMM_GP model consists of four stages and is illustrated in Figure 2, as adapted from [58]. The colour schematics applied in Figure 2 represent the key four stages of the HMM_GP modelling framework which are briefly described below and can found elsewhere [58] for underlying technical details. Grey colour boxes are used for highlighting data pre-processing and post-processing steps, whereas purple boxes indicate the start and end of the procedure.…”

“…The data pre-processing is intended to stabilise variance in non-stationary series and to de-emphasise the influence of extreme values (outliers). A detailed literature review covering a detailed overview of the STL method, along with other closely related mathematical techniques for time series decomposition, such as empirical mode decomposition (EDM), can be found elsewhere [58,60,61].…”