The objectives of the European Energy transition entail an increasing use of electricity especially for residential sector. Member states are invited to promote energy policies that involve stakeholders directly. Energy Communities (EC) are intended as local institutions that could drive this change, creating local-scaled energy entities that cooperate to exchange energy. The purpose of this study is to investigate the energy consumption identifying a linear regression model to forecast electric energy demand at municipal scale, for residential end users. This work analyses electric consumption of 1,201 municipalities in Piedmont (north-west of Italy) evaluating the main energy-related variables. Information are obtained by online databases and georeferenced with GIS tool. The identified model evidences that the most influential variables are the population, the number of members per family, the education level, and the income. Regarding building features, the dwelling area and the number of occupied dwellings, the age of buildings and their maintenance condition. The statistical GIS-based methodology proposed in this study is replicable and can be applied to other contexts. A forecasting model to predict the amount of energy demand can support preliminary decision-making process defining the scale of ECs and their optimal configuration for balancing energy demand and local production.
Statistical Data Analysis for Energy Communities / Mutani, Guglielmina; Santantonio, Silvia; Tartaglia, Angelo. - In: TECNICA ITALIANA. - ISSN 0040-1846. - ELETTRONICO. - 64:2-4(2020), pp. 385-397.
|Titolo:||Statistical Data Analysis for Energy Communities|
|Data di pubblicazione:||2020|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.18280/ti-ijes.642-438|
|Appare nelle tipologie:||1.1 Articolo in rivista|