A methodology to estimate space heating and domestic hot water energy demand profile in residential buildings from low-resolution heat meter data

Daniel Leiria, Hicham Johra, Anna Marszal-Pomianowska and Michal Zbigniew Pomianowski

Energy, 2023, vol. 263, issue PB

Abstract: This article presents a new methodology to disaggregate the energy demand for space heating (SH) and domestic hot water (DHW) production from single hourly smart heat meters installed in Denmark. The new approach is idealized to be easily applied to several building typologies without the necessity of in-depth knowledge regarding the dwellings and their occupants. This paper introduces, tests, and compares several algorithms to separate and estimate the SH and DHW demand. To validate the presented methodology, a dataset of 28 Danish apartments with detailed energy monitoring (separated SH and DHW usage) is used. The comparison shows that the best method to identify energy demand data points corresponding to DHW production events is the so-called “maximum peaks” approach. Furthermore, the best algorithm to estimate the SH and DHW separately is a combination of two methods: the Kalman filter and the Support Vector Regression (SVR). This new methodology outperforms the current Danish compliances typically used to estimate the annual DHW usage in residential buildings.

Keywords: Smart energy meter; Data disaggregation; Building energy usage; Load profiles; Time series analysis; District heating (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:263:y:2023:i:pb:s0360544222025919

DOI: 10.1016/j.energy.2022.125705

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