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Smart meters are currently being rolled out in European district heating (DH) systems at a large scale to enable time-varying district heating tariffs and improve consumer awareness about their own consumption. Smart-meter data can also be used in more advanced applications, e.g. for establishing model-based control schemes for demand response purposes and data-driven building energy performance labeling schemes. Many of these applications require separate measurements of the consumption for space heating (SH) and preparation of domestic hot water (DHW); however, smart meters often only provide the total DH energy consumption (SH+DHW) in truncated units (e.g. whole kWh on an hourly basis). Typical approaches for separating these two components of DH consumption require measurements with a high temporal and numerical resolution and are therefore not applicable to smart-meter data. New methods suitable for disaggregating the combined DH demand are therefore needed. This paper presents a validation of a model-based method for disaggregating DH consumption using ground truth data from 44 residential buildings.

Experimental validation of a model-based method for separating the space heating and domestic hot water components from smart-meter consumption data