System of statistical approaches for community energy demand modelling

Modelling transient community-level peak energy demand event is often challenging, as it requires the acquisition and systematic analysis/modelling of electricity demand data across a large number of buildings. Electricity demand data with diverse demand characteristic can be analysed/modelled/aggregated (in time) to understand the impact of various micro-level activities (specifically, peak demand household-level activities occurring simultaneously across multiple dwelling at a specific time) on the community-level demand curve. However, in real-life applications, the availability of good-quality electricity demand data across a large number of multiple dwellings within a community is often challenging. This paper is aimed to investigate the potentials of kmeans clustering approach for developing a systematic sampling, weighting and demand aggregation strategy for projecting community-level demands with high precision, just by using a small sample of buildings and easily accessible contextual information (e.g. average monthly demand or various activity periods during a day). These selected samples of dwellings are processed with a novel system of demand synthesis model developed by authors, referred to as HMM_GP. Five different variants of kmeans clustering are developed using statistical mean, median and proportion of demand during four different periods of days. Corresponding to each variant five aggregation schemes are constructed. The HMM_GP model is underpinned by a hidden Markov model (HMM) for simulating synthetic demand and a Generalised Pareto (GP) distribution to effectively model dynamics of peak demand events. Aggregation schematics are demonstrated for 30-minutely demand dataset collected over four weeks in July 2017 for 74 dwellings for a casestudy community of Fintry (Scotland).