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.
Highlights
Enhancing energy efficiency has become a priority for the European Union (Anon, 2018)
Kontokosta and Tull (2017) developed a predictive energy use model at the building, district, and city scales using training data from energy disclosure policies and predictors from the widely available property and zoning information. Their method was validated in New York, and the results demonstrated that electricity consumption could be reliably predicted using real data from a relatively small subset of buildings
To show the web dashboard created for this purpose, a set of examples are described in the following paragraphs
Summary
Enhancing energy efficiency has become a priority for the European Union (Anon, 2018). Oliveira Panão and Brito (2018) developed a bottom-up approach to model the aggregated hourly electricity consumption based on a Monte Carlo model They used probability distribution functions of the building stock characteristics, web surveys for user behaviour characterisation and energy consumption data from national statistics and smart meters data sets as input of the model. An innovative implementation of multiple statistical techniques to model the buildings stock energy consumption is performed It is based on inferring knowledge from actual weather data, aggregated consumption data from smart meters and building stock and socioeconomic characteristics data. The aim is to obtain normalised energy trends and KPIs to describe the energy consumption of each analysed region - e.g. yearly consumption per built area or monthly-averaged daily load curve due to heating or cooling needs The final goal is to provide a geographically aggregated characterisation methodology for building performance and usage trends of electricity consumption, both for the residential and public/tertiary buildings
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