The research paper A data-driven method for unsupervised electricity consumption characterisation at the district level and beyond has been published in the Journal "Energy Reports" (Volume 7, pages 5667-5684, November 2021). This paper is an effort of the ELISE Energy & Location Applications team in developing a methodology to better understand the drivers of electricity use for the public and private sector.
The European Union has created policies to enhance energy efficiency as a priority. These initiatives aim to improve buildings’ energy performance and collect data of sufficient quality on the effect of energy efficiency policies on building stock across Europe. Knowledge about the energy characteristics of buildings and their occupants’ usage is essential to define and assess strategies for energy conservation.
The paper presents a bottom-up electricity characterisation methodology of the building stock at the local level. It is based on the statistical learning analysis of aggregated energy consumption data, weather data, cadastre, and socioeconomic information. The methodology was implemented and tested by the characterisation of the electricity consumption of the whole province of Lleida, located in northeast Spain
The geographical aggregation level was done at the postal code level and the experimental test was supported by a web application environment specifically developed for this purpose.
The paper’s novelty relies on the application of statistical data methods able to infer the main energy performance characteristics of a large number of urban districts without prior knowledge of their building characteristics and with the use of solely measured data coming from smart meters, cadastre databases and weather forecasting services.
The potential reuse of this methodology allows for a better understanding of the drivers of electricity use, with multiple applications for the public and private sector.
To learn more about "Scale-up methodologies" applied in the Energy sector, click here.