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A data-driven method for unsupervised electricity consumption characterisation at the district level and beyond
- Source :
- Energy Reports, Vol 7, Iss, Pp 5667-5684 (2021), Repositorio Abierto de la UdL, Universitad de Lleida
- Publication Year :
- 2021
- Publisher :
- Elsevier, 2021.
-
Abstract
- 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. This work emanated from research conducted with the fi-nancial support of the European Commission through the H2020project BIGG , grant agreement 957047, and the JRC Expert Con-tractCT-EX2017D306558-102.D.ChemisanathanksICREAfortheICREA Acadèmia. Dr J. Cipriano also thanks the Ministerio deCiencia e Innovación of the Spanish Government for the Juan dela Cierva Incorporación grant
- Subjects :
- Consumo energético
Energy & Fuels
Computer science
020209 energy
Cadastre
Building-stock models
Weather forecasting
2202.03 Electricidad
02 engineering and technology
Reuse
computer.software_genre
3311.06 Instrumentos Eléctricos
Data-driven
Transport engineering
Key Performance Indicators (KPIs)
020401 chemical engineering
Electricity
0502 economics and business
11. Sustainability
0202 electrical engineering, electronic engineering, information engineering
Edificación residencial
050207 economics
0204 chemical engineering
1203.26 Simulación
Consumption (economics)
050208 finance
Geolocalización
business.industry
Characterisation
Building-stockmodels
05 social sciences
Energy consumption
Simulación energética - herramientas
Caracterización energética
Comportamiento energético
TK1-9971
3305.14 Viviendas
2202.02 Magnitudes Eléctricas y Su Medida
General Energy
3311.02 Ingeniería de Control
Clima
Performance indicator
Electrical engineering. Electronics. Nuclear engineering
Electricidad
business
computer
Subjects
Details
- Language :
- English
- ISSN :
- 23524847
- Volume :
- 7
- Database :
- OpenAIRE
- Journal :
- Energy Reports
- Accession number :
- edsair.doi.dedup.....c9a4c8c7bf0013e6d96a439d6d55c977