Time-Use Data Modelling of Domestic, Commercial and Industrial Electricity Demand for the Scottish Islands

Chris Matthew () and Catalina Spataru
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Chris Matthew: Energy Institute, Bartlett School of Environment, Energy and Resources, University College London, London WC1H 0NN, UK
Catalina Spataru: Energy Institute, Bartlett School of Environment, Energy and Resources, University College London, London WC1H 0NN, UK

Energies, 2023, vol. 16, issue 13, 1-25

Abstract: Achieving emissions reduction targets requires improved energy efficiency to avoid an oversized and excessively expensive electricity network. This can be analysed using hourly demand modelling that captures behaviour profiles, technology types, weather factors and building typologies. Numerous domestic models exist, but whole systems energy modelling, including commercial and industrial demand, are limited by data availability. Time-use survey data has typically been used to model domestic demand- in this work is expanded to also model commercial and industrial electricity-heating for the Scottish islands at an hourly and individual building level. This method is widely applicable for modelling whole system energy demand wherever time-use survey data are available. Combinatorial optimisation has been applied to generate a synthetic population, match individuals to properties and apply construction types to building polygons. SimStock is used for heating and lighting modelling. Validation of the model with 2016 data shows that it reflects longer term trends, with a monthly mean absolute percentage error (MAPE) of 1.6% and an R 2 of 0.99. At the hourly level, the MAPE of 6.2% and R 2 of 0.87 show the model captures variability needed to combine it with a supply-side model. Dataset accuracy, variability in the date recorded, missing data and unknown data correlations are discussed as causes for error. The model can be adapted for other regions and used to analyse the costs and benefits of energy efficiency measures with a supply-side generation model.

Keywords: electricity demand modelling; Scottish Islands; hourly demand; time-use data; demand forecasting; domestic; commercial and industrial (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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