Abstract
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.
Highlights
In an effort to improve the transparency and billability of energy consumption, a recent amendment to the energy efficiency directive (EED) of the European Union (EU) introduced a requirement for all newly installed district heating (DH) and cooling meters to be remotely readable by 25 October 2020, while all remaining non-remotely readable meters is to be replaced by 1 January 2027 [1]
We present a validation of a method recently proposed by Hedegaard et al [9] in which total district heating measurements from a building are disaggregated into space heating (SH) and DHW, respectively, through parallel calibration of a model of the thermal dynamics of the building and a model of the domestic hot water consumption
The results show that the model-based method is capable of identifying the DHW consumption patterns with reasonable accuracy
Summary
In an effort to improve the transparency and billability of energy consumption, a recent amendment to the energy efficiency directive (EED) of the European Union (EU) introduced a requirement for all newly installed district heating (DH) and cooling meters to be remotely readable by 25 October 2020, while all remaining non-remotely readable meters is to be replaced by 1 January 2027 [1]. The improved availability of high-resolution smart-meter consumption data is one of the cornerstones in enabling several new technologies to be implemented in building automation One of these technologies is model-based control schemes for heating systems, which may improve the comfort of occupants while minimizing energy consumption by optimizing HVAC control [2,3]. The models used to establish such control schemes need to describe the thermal dynamic behavior of the buildings accurately and are typically obtained by calibrating the parameters of said models using measurements of internal temperatures, external weather conditions, and heating consumption Since the latter of these measurements is often the hardest to obtain, the availability of data from already installed smart meters may be a decisive factor for the viability of such control schemes. A third approach, which is not further discussed in this study due to a lack of water consumption data, relies on measurements of the cold water consumption together with the assumption that a certain fraction of the consumed water is heated before use
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