Impacts of Size and History Length on Energetic Community Load Forecasting: A Case Study

Abstract

Recently, most European distribution systems (DS) are overwhelmed by the coupled growth of decentralized production and residential appliance volatility. To cope with this issue, new solutions are emerging, such as local energy storage and energetic community management. The latter aims for the collective self-consumption maximization of the locally-produced energy through optimal planning of flexible appliances, to reduce DS maintenance costs and energy loss. The quality of short-term load forecasting is key in this process. However, it depends on various factors, foremost including the characteristics of the concerned energetic community. In this paper, we propose a methodology and a use case, based on randomized sampling for the simulation of virtual energetic communities (VEC). From the numerous simulated VEC, statistical analysis allows to assess the impact of the VEC characteristics (such as size, resident type and availability of historical data) on its predictability. From a 2-year dataset of 52 households recorded in a Belgian city, we quantify the impacts of these characteristics, and show that for this specific case study, a trade-off for efficient forecasting can be reached for a community of about 10-30 households and 2-12 months of history length.