A data-driven method for unsupervised electricity consumption characterisation at the district level and beyond
MetadataShow full item record
A bottom-up electricity characterisation methodology of the building stock at the local level is presented. It is based on the statistical learning analysis of aggregated energy consumption data, weather data, cadastre, and socioeconomic information. To demonstrate the validity of this methodology, the characterisation of the electricity consumption of the whole province of Lleida, located in northeast Spain, is implemented and tested. The geographical aggregation level considered is the postal code since it is the highest data resolution available through the open data sources used in the research work. The development and the experimental tests are supported by a web application environment formed by interactive user interfaces 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. A data-driven technique disaggregates electricity consumption in multiple uses (space heating, cooling, holidays and baseload). In addition, multiple Key Performance Indicators (KPIs) are derived from this disaggregated energy uses to obtain the energy characterisation of the buildings within a specific area. 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.
Is part ofEnergy Reports, 2021, vol. 7, p. 5667-5684
European research projects
The following license files are associated with this item:
Showing items related by title, author, creator and subject.
Mor, Gerard; Cipriano, Jordi; Gabaldon Ponsa, Eloi; Grillone, Benedetto; Tur, Mariano; Chemisana Villegas, Daniel (MDPI, 2021)Thermostatic load control systems are widespread in many countries. Since they pro vide heat for domestic hot water and space heating on a massive scale in the residential sector, the assessment of their energy performance ...
Mor, Gerard; Vilaplana Mayoral, Jordi; Danov, Stoyan; Cipriano, Jordi; Solsona Tehàs, Francesc; Chemisana Villegas, Daniel (Institute of Electrical and Electronics Engineers (IEEE), 2018)This paper presents the EMPOWERING project, a Big Data environment aimed at helping domestic customers to save electricity by managing their consumption positively. This is achieved by improving the information received ...
Baseline Energy Use Modeling and Characterization in Tertiary Buildings Using an Interpretable Bayesian Linear Regression Methodology Grillone, Benedetto; Mor, Gerard; Danov, Stoyan; Cipriano Líndez, Jorge; Lazzari, Florencia; Sumper, Andreas (MDPI, 2021)Interpretable and scalable data-driven methodologies providing high granularity baseline predictions of energy use in buildings are essential for the accurate measurement and verification of energy renovation projects ...