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


== Abstract ==

<p>Enhancing energy efficiency has become a priority for the European Union. Several policies and initiatives aim<br>
to improve the energy performance of buildings and collect data of sufficient quality on the effect of energy<br>
efficiency policies on building stocks across Europe. Knowledge about the characteristics of the building stock<br>
and the usage of these buildings&#39; occupants is essential for defining and assessing strategies for energy savings.<br>
Nowadays, dynamic measured data from the Advanced Metering Infrastructure (AMI), especially in electricity<br>
consumption, combined with location-based data, like weather, cadastre, social or economic conditions, should<br>
be available for a significant part of the building stocks in Europe. Combinedly, this enormous set of data<br>
contains the characteristics of how buildings and their occupants consume energy.<br>
In this document, a bottom-up electricity characterisation methodology of the building stock at the local level<br>
is presented. It is based on the statistical analysis of aggregated energy consumption data, weather data,<br>
cadastre, and socioeconomic information. For validation purposes, the characterisation of the electricity<br>
consumption over Lleida (Spain) province is performed. The geographical aggregation level considered is the<br>
postal code (more detailed than LAU level 2, formerly NUTS level 5), due to it is the highest resolution available<br>
through the Spanish Distribution System Operators (DSOs) data portal. Besides, a web application to visualise<br>
the results of the characterisation has also been developed. The major novelty is the use of high-frequency<br>
consumption data from most consumers in each analysis area without considering any Building Energy<br>
Simulation (BES) model that considers performance or energy use assumptions. For this purpose, a data-driven<br>
technique is used to disaggregate consumption due to multiple components (heating, cooling, holiday and<br>
baseload). In addition, multiple Key Performance Indicators (KPIs) are derived from these components to obtain<br>
the characterisation results. The potential reuse of this methodology allows for a better understanding of the<br>
drivers of electricity use, with multiple applications for the public and private sectors.<br>
This study has been executed in the frame of the Energy &amp; Location Applications of the ELISE (European<br>
Location Interoperability Solutions for e-Government) action of the ISA 2 (Interoperability solutions for public<br>
administrations, businesses and citizens) Programme.</p>



== Full document ==
<pdf>Media:Draft_Mor Martinez_823501223-2374-document.pdf</pdf>