Data-driven method for unsupervised electricity consumption characterisation at district level and beyond

Abstract

Enhancing energy efficiency has become a priority for the European Union. Several policies and initiatives aim
to improve the energy performance of buildings and collect data of sufficient quality on the effect of energy
efficiency policies on building stocks across Europe. Knowledge about the characteristics of the building stock
and the usage of these buildings' occupants is essential for defining and assessing strategies for energy savings.
Nowadays, dynamic measured data from the Advanced Metering Infrastructure (AMI), especially in electricity
consumption, combined with location-based data, like weather, cadastre, social or economic conditions, should
be available for a significant part of the building stocks in Europe. Combinedly, this enormous set of data
contains the characteristics of how buildings and their occupants consume energy.
In this document, a bottom-up electricity characterisation methodology of the building stock at the local level
is presented. It is based on the statistical analysis of aggregated energy consumption data, weather data,
cadastre, and socioeconomic information. For validation purposes, the characterisation of the electricity
consumption over Lleida (Spain) province is performed. The geographical aggregation level considered is the
postal code (more detailed than LAU level 2, formerly NUTS level 5), due to it is the highest resolution available
through the Spanish Distribution System Operators (DSOs) data portal. Besides, a web application to visualise
the results of the characterisation has also been developed. The major novelty is the use of high-frequency
consumption data from most consumers in each analysis area without considering any Building Energy
Simulation (BES) model that considers performance or energy use assumptions. For this purpose, a data-driven
technique is used to disaggregate consumption due to multiple components (heating, cooling, holiday and
baseload). In addition, multiple Key Performance Indicators (KPIs) are derived from these components to obtain
the characterisation results. 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 sectors.
This study has been executed in the frame of the Energy & Location Applications of the ELISE (European
Location Interoperability Solutions for e-Government) action of the ISA 2 (Interoperability solutions for public
administrations, businesses and citizens) Programme.

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