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.

本文提出了一种新型的数据驱动方法系统，用于模拟电力需求曲线的动态。使用STL（基于黄土的季节性趋势分解程序）方法，将单个住宅的需求概况分解为确定性（例如“趋势”和“季节性”）和随机（“剩余”）部分。随机成分使用隐马尔可夫模型（HMM）建模，并与确定性成分组合以生成综合需求曲线。为了模拟极端（峰值）需求，使用通用帕累托（GP）分布和基于百分位的偏差校正方案对合成轮廓进行了后处理。所有技术都系统地耦合到称为“ STL_HMM_GP”的混合系统中。通过比较三个案例研究社区的观察和模拟剖面的统计特征范围，可以全面访问和验证STL_HMM_GP系统。演示了STL_HMM_GP系统的潜力，可用于模拟汇总的需求概况，该需求概况是使用观察到的单个需求概况的可访问性小样本生成的。