Splitting measurements of the total heat demand in a hotel into domestic hot water and space heating heat use

Ivanko, Dmytro; Sørensen, Åse Lekang; Nord, Nataša

doi:10.1016/j.energy.2020.119685

Cited by 22 publications

(15 citation statements)

References 22 publications

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“…In addition, a slight correlation between DHW heat load and outdoor temperature has also been demonstrated [6], [7]. Notably, Ivanko et al's study in Norwegian hotels underscores that DHW energy demand is minimally affected by outdoor temperature, with guest count being the primary variable [8].…”

Section: Introductionmentioning

confidence: 94%

“…Instead, the segment with a temperature above CPT represents the energy consumption related to the warm season, where SH is unnecessary, and only DHW is produced. It shows minimal dependence on outdoor air temperature [8]. Some studies separate temperature-dependent and temperature-independent segments by demand instead of based on outdoor air temperature [10].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A data-driven model for the analysis of energy consumption in buildings

Borgato,

Prataviera,

Bordignon

et al. 2024

E3S Web Conf.

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Data-driven models are gaining traction in Building Energy Simulation, driven by the increasing role of smart metering and control in buildings. This paper aims to enhance the knowledge in this sector by introducing a practical method to analyse heating consumption. The methodology involves the analysis of hourly total heating demand and outdoor temperature measurements to create and calibrate Energy Signature Curves. Importantly, the building Energy Signature Curve is calibrated independently for each daily hour, resulting in a subset of 24 data-driven models. After calibration, a disaggregation algorithm is proposed to distinguish space heating from domestic hot water usage. The method also evaluates the building’s thermal inertia, examining the correlation between the hourly global energy consumption and the outdoor air temperature moving average. It also presents a methodology for improving the DHW heat consumption model. The methodology is applied to a case study of 51 buildings in Tartu, Estonia, with complete yearly demand measurements from the district heating operator. Thanks to the hourly calibration approach, R2 is 0.05 higher on average than the yearly Energy Signature Curve approach. The difference between estimated and measured annual energy consumption is 8% on average, demonstrating the practicality and effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

A data-driven model for the analysis of energy consumption in buildings

Borgato,

Prataviera,

Bordignon

et al. 2024

E3S Web Conf.

View full text Add to dashboard Cite

show abstract

“…In [22], another approach is proposed to separate the different measurements in a Norwegian hotel. Two methods were presented and compared.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Pereira

Johra

Marszal-Pomianowska

et al. 2023

Energy

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“…Unlike the above method, more straightforward methods were developed to disaggregate heating datasets. The articles Lien, Ivanko andSartori (2020), andIvanko, Sørensen andNord (2021) propose different methods to decompose SH and DHW usage based on discovering the DHW profiles when the total heating is assumed to be equal to the DHW usage only (no SH demand). Additionally, considering the relationship between SH demand and the external temperature, the methods were validated with several Norwegian buildings (apartments and hotels) and compared with other existing methods.…”

Section: Introductionmentioning

confidence: 99%

Validation of a new method to estimate energy use for space heating and hot water production from low-resolution heat meter data

Pereira

Johra²,

Belias³

et al. 2022

E3S Web Conf.

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One of the initiatives to reach the European decarbonization goal is the roll-out of smart heating meters in the building stock. However, these meters often record the total energy usage with only hourly resolution, without distinguishing between space heating (SH) and domestic hot water (DHW) production. To tackle this limitation, this paper presents the validation of a new methodology to estimate the SH and DHW from total measurements in different building types in three countries (Denmark, Switzerland, and Italy). The method employs a combined smoothing algorithm with a support vector regression (SVR) to estimate the different heating uses. The estimation results are compared with the different countries’ DHW compliance calculations. The comparison showed that the compliance calculations outperformed this method by considering the validation dataset characteristics.

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Splitting measurements of the total heat demand in a hotel into domestic hot water and space heating heat use

Cited by 22 publications

References 22 publications

A data-driven model for the analysis of energy consumption in buildings

A data-driven model for the analysis of energy consumption in buildings

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

Validation of a new method to estimate energy use for space heating and hot water production from low-resolution heat meter data

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