A review of domestic hot water consumption profiles for application in systems and buildings energy performance analysis

Fuentes, E.; Arce, L.; Salom, Jaume

doi:10.1016/j.rser.2017.05.229

Cited by 229 publications

(152 citation statements)

References 55 publications

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“…It was observed that most users tend to take a shower after 18:00, whereas some tend to take a shower both in the morning and in evening. Similar shower habits have also been reported by Fuentes et al…”

Section: Methodssupporting

confidence: 90%

“…The shower habits of seven users are analyzed to find the main influencing factors. Fuentes et al reported that daily and seasonal variables (ie, shower time/hour and month) are the most significant factors . In addition, the time interval between showers is also considered in this study.…”

Section: Methodsmentioning

confidence: 96%

“…The start time is defined as the shower time, whereas the end time is used to calculate the duration of a shower. Water consumption per shower is calculated based on the duration of the shower, at a water flow speed of 500 L/hour (8.33 L/minute) …”

Section: Methodsmentioning

confidence: 99%

“…The relationship between hot water consumption and the seasonal variable is investigated. Hot water consumption is strongly related with the month mainly because of the seasonal changes in the environmental temperature . Many researches have reported a trend in which hot water consumption will increase as the environmental temperature decreases .…”

Section: Methodsmentioning

confidence: 99%

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Predictive control based on occupant behavior prediction for domestic hot water system using data mining algorithm

Cao

Hou

et al. 2019

Energy Science & Engineering

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Domestic hot water systems are a primary source of energy consumption in buildings, and present a promising future in terms of energy saving. Predictive control methods have been used to reduce energy consumption during an operation period. However, current methods lack consideration of occupant behavior, which significantly influences the prediction results. In this study, a data‐based predictive method is proposed to predict the shower behavior of occupants. A dataset was collected from seven occupants and was trained using support vector machine (SVM) to learn their showering habits. These results were used to predict the hot water demand. A comparative analysis shows that, in this way, the prediction results are more reliable under predictive control than predicting the hot water demand directly using SVM. A simulation of a domestic hot water system was then conducted. The results indicate that the proposed approach can reduce the heat loss from a hot water storage tank (ST) by up to 33% compared to a traditional control method, while maintaining a high assurance of the hot water supply (an insufficient hot water supply of less than 1%) with little change to the original system.

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Section: Methodssupporting

confidence: 90%

Section: Methodsmentioning

confidence: 96%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Predictive control based on occupant behavior prediction for domestic hot water system using data mining algorithm

Cao

Hou

et al. 2019

Energy Science & Engineering

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“…However, there is an increasing concern within academia, industry and policy-making that in practice, energy efficiency standards are not being achieved [3], while a growing body of evidence suggests that domestic and non-domestic buildings often underperform as compared to the design specifications [4,5]. The so called energy performance gap between the design intent and the actual energy use in domestic buildings is the result of multiple factors, including occupant behaviour, building fabric thermal performance and actualoccupants may determine large variations of energy use, since they affect the choice and control of heating and cooling systems [6], the use of hot water for baths and showers [7,8] and the use of electric appliances [9]. The extent of the energy performance gap in residential building retrofits in Germany has been found to be as high as 300% in comparison to the expected energy savings [10].…”

Section: Introductionmentioning

confidence: 99%

Magnitude and extent of building fabric thermal performance gap in UK low energy housing

Gupta

Kotopouleas

2018

Applied Energy

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• Air-permeability, U-value and whole house heat loss data were statistically tested.• Building fabric thermal performance gap was widespread in low energy dwellings.• Airtightness gap was trivial in Passivhaus but significant in non-Passivhaus units.• Gap increased by 0.8 m 3 /h/m 2 for every 1 m 3 /h/m 2 decrease in design air permeability.• Building regulations should require in-situ tests to reduce fabric performance gap. ARTICLE INFO Keywords:Building fabric Low energy dwellings Passivhaus Airtightness Heat loss Thermal transmittance Co-heating test ABSTRACT This paper presents new evidence from a nationwide cross-project meta-study investigating the magnitude and extent of the difference between designed and measured thermal performance of the building fabric of 188 low energy dwellings in the UK. The dataset was drawn from the UK Government's national Building Performance Evaluation programme, and comprises 50 Passivhaus (PH) and 138 non-Passivhaus (NPH) dwellings, covering different built forms and construction systems. The difference between designed and measured values of air permeability (AP), external wall/roof thermal transmittance (U-value) and whole house heat loss were statistically analysed, along with a review of thermal imaging data to explain any discrepancies. The results showed that fabric thermal performance gap was widespread especially in terms of AP, although the magnitude of underperformance was much less in PH dwellings. While measured AP had good correlation with measured space heating energy for PH dwellings, there was no relationship between the two for NPH dwellings. The regression analysis indicated that for every 1 m 3 /h/m 2 reduction in designed air permeability, the gap increased by 0.8 m 3 /h/m 2 @50 Pa. Monte Carlo analysis showed that likelihood of AP gap was 78% in NPH dwellings designed to 5 m 3 /h/m 2 @50 Pa or lower. The study provides useful evidence for improving the fabric thermal performance of new housing through in-situ testing.

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Dynamic testing of a new integrated collector storage solar water heater (ICSSWH) according to standard ISO 9459‐5: Use of a numerical approach for long‐term prediction in different climatic conditions

Messaouda,

Hamdi,

Hazami

et al. 2023

Env Prog and Sustain Energy

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This work analyzes the performance of a new Integrated Collector Storage Solar Water Heater (ICSSWH) by using Dynamic System Testing (DST) method according to Standard ISO 9459‐5. The long‐term performance study of the system in different climatic conditions has been achieved by a coupling model between DST results and a numerical approach based on the system transient behavior. Results of the experimental dynamic system evaluation showed that the total store heat capacity Cs, the useful collector surface Ac* and the storage tank heat losses coefficient Us are, respectively, 0.87 MJ/K, 1.21 m2 and 10 W/K. In Tunisian climatic conditions, the ICSSWH system allows covering until 79.3% from a typical family need for a daily consumption of 170 L. The results found allow optimizing the energy gain of the ICSSWH as well as selecting a specific load volume according to the location. Especially, investigations showed that in Stockholm and Riadh climatic conditions, the ICSSWH's solar fraction reached 27% and 83%, respectively, for a daily load volume of 250 L. More importantly, the experimental effort of DST identification can be minimized by coupling DST data with a robust numerical approach.

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A review of domestic hot water consumption profiles for application in systems and buildings energy performance analysis

Cited by 229 publications

References 55 publications

Predictive control based on occupant behavior prediction for domestic hot water system using data mining algorithm

Predictive control based on occupant behavior prediction for domestic hot water system using data mining algorithm

Magnitude and extent of building fabric thermal performance gap in UK low energy housing

Dynamic testing of a new integrated collector storage solar water heater (ICSSWH) according to standard ISO 9459‐5: Use of a numerical approach for long‐term prediction in different climatic conditions

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