First results of remote building characterisation based on smart meter measurement data

Andreas Melillo, Roman Durrer, Jörg Worlitschek and Philipp Schütz

Energy, 2020, vol. 200, issue C

Abstract: In European households, 79% of the energy is consumed for space heating and cooling. The remote detection of possible retrofitting targets can help to increase the renovation rate and hence contribute to the realization of the 2000 W society. Here, a new method to characterize buildings based on smart meter monitoring data and a simplified physical simulation model is presented. The aim of this method is to estimate the time dependent demand of heating energy based on weather data applying these simplified building models. The method has been successfully applied on simulation and real-world smart meter monitoring data. The annual space energy demand was excellently reproduced with a deviation of less than 1% and 8% for simulation and real-world buildings, respectively. The recovered relevant building parameters deviate less than 1% for the reference case. The successful application of the algorithm on in-silico and real-world data monitoring data indicates the vast potential of this automated modelling technique on heat load prediction and energy-efficient operation of buildings. Furthermore, the derived heat demand profile may help utilities and facility managers in the future to identify better operation schedules of small areas and districts.

Keywords: Remote building characterisation; Heat load prediction; Smart meter monitoring data; Energy efficiency (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:200:y:2020:i:c:s0360544220306320

DOI: 10.1016/j.energy.2020.117525

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