Statistical Data-Driven Regression Method for Urban Electricity Demand Modelling

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Abstract

As the focus of the energy transition within cities worldwide moves towards local communities and neighbourhoods, the need for insights in the dynamics of local electricity demand increases. Detailed local electricity demand information is, however, often not available. This paper proposes a statistical data-driven method to model local electricity demand for mixed urban areas, using a combination of other openly available datasets. Such datasets however are mutually incompatible without further conversion. The proposed method over- comes this problem. Linear regression is used to combine these different datasets, whereby the regression coefficients have the meaning of scaling factors for different types of electricity consumers (households, offices, shops, etc.). The method is calibrated and validated using respectively a training and a test dataset of Dutch municipalities, yielding R-squared values for most consumer types between 61% and 98%. The application of the method for local electricity demand modelling is illustrated for three Dutch municipalities with different consumer compositions.
Original languageEnglish
Title of host publicationProceedings 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)
PublisherIEEE
ISBN (Print)978-1-5386-5185-8
DOIs
Publication statusPublished - 2018
Event2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe) - Palermo, Italy
Duration: 12 Jun 201815 Jun 2018

Conference

Conference2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)
CountryItaly
CityPalermo
Period12/06/1815/06/18

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