Statistical Data-Driven Regression Method for Urban Electricity Demand Modelling
Nina Voulis (TU Delft - Technology, Policy and Management)
Martijn Warnier (TU Delft - Technology, Policy and Management)
Frances Brazier (TU Delft - Technology, Policy and Management)
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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.