Modelling aggregate hourly electricity consumption based on bottom-up building stock

This paper presents a building stock energy model for the estimation of hourly electricity consumption for a large group of residential buildings. A Monte Carlo model stochastically generates a large sample of dwellings representative of the building stock and the correspondent number of user profiles, statistically supported by a web survey about the use of energy in dwellings for space heating and cooling. The model uses hourly energy balance equations to estimate energy needs and calculates the mean annual electricity consumption for regularly occupied dwellings with an error below 3%. Model is also validated against independent smart-metered data of about 250 dwellings. Hourly electricity consumption results feature an overall normalised mean absolute error of 11% and normalised root mean square error of 16%. The maximum relative difference is +/- 72% and the maximum absolute error is similar or equal to 217 MM. The model is considered to be able to predict hourly electricity consumption accurately. (C) 2018 Elsevier B.V. All rights reserved.