Domestic Hot Water Use Research Articles

Increased Understanding of Building Operational Performance Through Occupant-Centric Key Performance Indicators

This article introduces a novel framework for Occupant-Centric Key Performance Indicators (OC KPIs) which aims to increase the understanding of building performance by aligning with occupant presence during the operational phase. Unlike traditional Key Performance Indicators (KPIs), OC KPIs can better emphasize the usefulness of energy use in the building to provide services and indoor comfort to occupants when they are actually present. Within this framework, the study explores 52 traditional KPIs and OC KPIs focusing on heating use, electricity use, domestic hot and cold water use, thermal comfort, and air quality. The study case is a multi-story residential low-energy building located in Denmark (five apartments, 16 rooms). The findings suggest that incorporating occupancy data in the OC KPI calculations enables a deeper understanding of energy-related occupant behavior and Indoor Environmental Quality (IEQ). These include 1) the potential for installing a more advanced heating control algorithm by analyzing occupancy over time in relation to heating use, and 2) the identification of significant appliance-related behavior in correspondence with load matching, which can be used for predictive maintenance or personalized energy use feedback. Furthermore, OC KPIs show the potential to help detect insufficient IEQ, energy use inefficiencies due to system anomalies/faults and/or occupant behavior, and guide towards savings opportunities. Finally, this article discusses the practical implementation of OC KPIs, and compares the latter with traditional KPIs, thus supporting a paradigm shift towards a more occupant-centric assessment of building performance.

Performance analysis of compression-assisted absorption refrigeration-heating system for waste heat recovery of liquid-cooling data center

The power consumption for cooling of data centers increases continuously contributed to the enlargement of heat dissipation demands. Waste heat recovery is regarded as a promising solution for energy conservation and carbon reduction. A compression-assisted absorption refrigeration-heating system aimed at waste heat utilization of liquid-cooling data center is proposed. The effects of heat sources, auxiliary compression process and split ratios on the proposed system performance are discussed, and the analysis of application that synchronously produces chilled water for liquid-cooling data center and hot water for domestic use is conducted. The compression process can provide effective compensation for the system performance that weakens the demands on the quality of cooling source and driving heat source. As the compression ratio raises from 1.067 to 2.0, the minimum generation temperature decreases from 52.0 °C to 27.2 °C. The system can completely satisfy the refrigeration need, of 4.29 kW for liquid-cooling data centers and provide additional thermal energy, of 1.37 kW for heating. A quantitative relationship between the flow split ratios and circulation ratios is found out that can be a wise presentation for the adjustment of the refrigeration and heating loads. This research results can provide theoretical basis and guidance for low-grade waste heat recovery in liquid-cooling data center based on the absorption heat pump technology.

Disaggregation of total energy use into space heating and domestic hot water: A city-scale suited approach

This paper develops a computationally fast algorithm to disaggregate the total energy use recorded by smart heat meters into space heating (SH) and domestic hot water (DHW). The algorithm trains a regression model on SH-only hours and predicts SH for all other hours with potential DHW use. Data from smart water meters were used to identify hours with only SH. Assessing 13 regression models, an untuned random forest was identified as the best-performing regression model with an acceptable computational cost (median: 7.4s per building). Furthermore, using one year of data from over 2400 single-family homes, it was shown that the best result (median CVRMSE: 0.13) can be obtained using only smart heat meters based regressors, increasing the applicability of the developed method. Furthermore, two tests were implemented, and it was successfully demonstrated that they identify situations where the regressors are distributed differently for hours with SH only and hours with SH and DHW, to identify possible training bias; thus, buildings where the disaggregation is potentially unreliable. Validation against data from three single-family houses with known SH and DHW use confirmed the good performance of the method and that the performance can be estimated without ground truth data via nested cross-validation.

Experimental investigation thermal and exergy efficiency of photovoltaic/thermal system

Photovoltaic/Thermal (PV/T) systems are getting attention due to limitations in rooftop space, especially in apartment buildings. Those buildings must improve electricity generation from photovoltaic (PV) systems and hot water for domestic use. PV/T systems were investigated experimentally for Jordanian weather in this paper. Thermal and exergy efficiency was evaluated for commercially available PV/T panels under different connection configurations, open-loop, and closed-loop connections. Results showed a significant effect for the PV/T panel for Jordanian weather. Closed-loop performance can be improved if the heat collected by PV/T is extracted from the tank. Finally, the total efficiency of the PV/T system in an open-loop configuration is significantly higher, 76 %, than in a closed-loop one, 58 %, due to the improvement in thermal efficiency.

3E investigation and artificial neural network optimization of a new triple-flash geothermally-powered configuration

Geothermal systems are among the world's most well-appreciated sources for providing heat to generate power and exploit in numerous ways, attracting attention worldwide due to abundancy and renewability. The present investigation is dedicated to a cogeneration system providing power and hot water for domestic use. The novel system consists of three flash chambers and three turbines intended for power production. The heat source is geothermal subterranean reservoir. Furthermore, thermoelectric generators were creatively implemented as substitutions for condensers so to produce surplus power by utilizing waste heat. The proposed cycle was primarily analyzed with respect to the first and second laws of thermodynamics. As an additional novelty, entransy analysis was also included. After which, the findings were validated in accordance to previously verified scientific resources. The study revealed that the overall power production in the basic mode was 133 kW, 35.62 kW of which was generated by the TEGs. Further, the first and second law efficiencies for the integrated cycle were 26.8% and 64.8, respectively. The sum of entransy loss throughout the integrated system was 8.77MWK. Eventually, an artificial neural network optimization was performed to identify the optimum states of the system, where exergy efficiency and entransy loss were assigned as the objectives.

Carbon reduction technology pathways for existing buildings in eight cities

We work with policymakers in eight cities worldwide to identify technology pathways toward their near- and long-term carbon emissions reduction targets for existing buildings. Based on policymakers’ interests, we define city-specific shallow and deep retrofitting packages along with onsite photovoltaic generation potential. Without further grid decarbonization measures, stock-wide implementation of these retrofits in the investigated neighborhoods reduces energy use and carbon emissions by up to 66% and 84%, respectively, helping Braga, Dublin, Florianopolis, Middlebury, and Singapore to meet their 2030 goals. With projected grid decarbonization, Florianopolis and Singapore will reach their 2050 goals. The remaining emissions stem from municipalities not planning to electrify heating and/or domestic hot water use. Different climates and construction practices lead to varying retrofit packages, suggesting that comparable technology pathway analyses should be conducted for municipalities worldwide. Twenty months after the project ended, seven cities have implemented policy measures or expanded the analysis across their building stock.

Experimental validation of a state-of-the-art model predictive control approach for demand side management with a hot water heat pump

Hot water heat pumps are well suited for demand side management, as the heat pump market faced a rapid growth in the past years with the trend to decentralized domestic hot water use. Sales were accelerated through wants and needs of energy conservation, energy efficiency, and less restrictive rules regarding Legionella. While in literature the model predictive control potential for heat pumps is commonly shown in simulations, the share of experimental studies is relatively low. To this day, experimental studies considering solely domestic hot water use are not available. In this paper, the realistic achievable model predictive control potential of a hot water heat pump is compared to the standard hysteresis control, to provide an experimental proof. We show for the first time, how state-of-the-art approaches (model predictive control, system identification, live state estimation, and demand prediction) can be transferred from electric hot water heaters to hot water heat pumps, combined, and implemented into a real-world hot water heat pump setup. The optimization approach, embedded in a realistic experimental setting, leads to a decrease in electric energy demand and cost per unit electricity by approximately 12% and 14%, respectively. Further, an increase in efficiency by approximately 13% has been achieved.

The cost savings potential of controlling solar thermal collectors with storage for time-of-use electricity rates

Homes and commercial buildings account for roughly 40% of U.S. energy consumption. A significant portion of this energy consumption, and therefore utility cost, comes from domestic hot-water use. Solar collectors such as flat plate collectors, evacuated tubes, and combined photovoltaic/thermal panels, are renewable options that collect thermal energy and could help reduce utility costs. Hot water is generated by pumping a working fluid through the solar collector, where the fluid captures thermal energy via absorbed solar radiation incident on the collector surface. The collection of heat coincides with the availability of solar radiation, but the best time to use this thermal energy depends on the utility rate at a given time, which often changes throughout the day. In this work, we implement and simulate a control strategy using Transient System Simulation Tool (TRNSYS) that reduces utility costs by controlling when the system stores and uses thermal energy based on solar availability and time-of-use electricity rates. Our modeled results show that the control strategy can reduce utility costs associated with using hot water between 3% and 30%. We also show how the on-peak utility rates, the size and type of the solar collectors, and the location’s climate impact the results.

Space heating demand profiles of districts considering temporal dispersion of thermostat settings in individual buildings

Future energy infrastructures are expected to be smart, with the application of energy storage and demand-side management strategies. To size the energy storage and assess the flexibility of the heat demand in a district, insight into its heat demand profile is a prerequisite.A tool was developed to generate the hourly space heating demand of a district. A crucial element in the calculation is the behaviour of occupants, particularly the temporal dispersion of thermostat settings in individual buildings. This dispersion smooths out the morning (and evening) peaks in the space heating demand of a district when occupants turn up their thermostats.Hourly gas consumption data were obtained of 8077 buildings with gas fired boilers in 2020. A dynamic resistance capacitance (RC) building model of a single aggregated building representing the 8077 dwellings was used to calculate their space heating demand profile in 2020. A comparison with the actual gas consumption data (corrected for domestic hot water use and cooking) allowed the estimation of the temporal thermostat settings, the dispersion thereof and the thermostat temperature levels of buildings in the district. The combined space heating demand profile of the individual buildings calculated using the same RC building model (each with its own thermostat settings) reproduced the actual space heating demand profile relatively well.

Estimating residential hot water consumption from smart electricity meter data

Residential water heating is among the most energy-intensive aspects of the water sector; however, residential hot water use is often poorly quantified. Estimating hot water consumption from smart electricity meter data can help advance the body of knowledge regarding the residential energy-water nexus by employing data to fill this knowledge gap, potentially promoting community resilience through energy and water resources efficiency. Using a non-intrusive load monitoring algorithm calibrated with fine-resolution data, we disaggregated electricity for water heating from half-hourly smart electricity meter data, demonstrated with data organized at the zip code level for areas in the city of Chicago. From these electricity for water heating signals, we estimated residential hot water consumption with quantified uncertainty. Results indicate that water heating accounted for 7%–20% of total electricity consumption in the analyzed single-family residential homes, representing an average of 1–8 kWh d−1 of electricity consumption and 7–55 gallons (26–208 l) of hot water per day. These results also demonstrated significant spatial variability, such that some areas of Chicago show higher per household hot water use. With the challenges of deploying advanced water metering infrastructure, using isolated water heating signals from smart electricity meters to develop a first-order estimate of domestic hot water use represents a valuable quantification of an energy-intense flow.

Energy and Exergy Analyses of a Novel Combined Heat and Power System Operated by a Recuperative Organic Rankine Cycle Integrated with a Water Heating System

This study reports the thermodynamic analysis of a high-temperature recuperative organic Rankine cycle comprising a water heating system that can provide a net power of 585.7 kW and hot water for domestic use at 35 °C. The performance was analysed using seasonal ambient temperature and water temperature data from Seoul, South Korea. The working fluid was separated into two different mass fractions after emerging from the turbine 1 outlet; one fraction provided heat to recuperate the organic Rankine cycle, and the other fraction was transferred to the water heating system for heating water. Mass fractions were balanced based on the projected seasonal need for hot water. Four working fluids with high critical temperatures and five working fluids with low critical temperatures were examined for top and bottom cycles, respectively. Chlorobenzene was selected for the top cycle and R601 was selected for the bottom cycle. The system achievement in individual months was analysed using thermal efficiency and exergy efficiency. Moreover, the performances of the hottest (low hot water demand) and coldest (high hot water demand) months were analysed.

Long-Term Evaluation of Comfort, Indoor Air Quality and Energy Performance in Buildings: The Case of the KTH Live-In Lab Testbeds

Digitalization offers new, unprecedented possibilities to increase the energy efficiency and improve the indoor conditions in buildings in a cost-efficient way. Smart buildings are seen by many stakeholders as the way forward. Smart buildings feature advanced monitoring and control systems that allow a better control of the buildings’ indoor spaces, but it is becoming evident that the massive amount of data produced in smart buildings is rarely used. This work presents a long-term evaluation of a smart building testbed for one year; the building features state-of-the-art monitoring capability and local energy generation (PV). The analysis shows room for improving energy efficiency and indoor comfort due to non-optimal control settings; for instance, average indoor temperatures in all winter months were above 24 °C. The analysis of electricity and domestic hot water use has shown a relevant spread in average use, with single users consuming approximately four times more than the average users. The combination of CO2 and temperature sensor was sufficient to pinpoint the anomalous operation of windows in wintertime, which has an impact on energy use for space heating. Although the quantification of the impact of users on the overall energy performance of the building was beyond the scope of this paper, this study showcases that modern commercial monitoring systems for buildings have the potential to identify anomalies. The evidence collected in the paper suggests that this data could be used to promote energy-efficient behaviors among building occupants and shows that cost-effective actions could be carried out if data generated by the monitoring and control systems were used more extensively.

Numerical assessment of self-sufficiency of residential buildings in Belgium by using heat pumps, photovoltaic panels and energy storages

Residential buildings claim a significant share of the total energy use worldwide. In order to have more realistic energy performance predictions, increased attention is paid to the analysis of the building’s energy use through comprehensive, transient detailed numerical simulations. In this article, the self-consumption and self-sufficiency values of three detached residential buildings are assessed through numerical models made in the programming language Modelica and software tool Dymola. The three buildings have the same structure and different space heating energy demands of 15 kWh/m2year, 30 kWh/m2year and 45 kWh/m2year. The energy use of the buildings coincides with the occupancy profile where domestic hot water use dominates over the space heating demand provided by an air to water heat pump. The discrepancy between renewable energy production and energy consumption is mitigated by means of thermal load shifting and electrical energy storage. In this research, the self-consumption and self-sufficiency of the studied buildings have been analysed as a function of the economically favourable energy storage sizing. For the use of an electrical battery with the installed capacity of 2.5 kWh and thermal energy storage of 250 l, the self-sufficiency results to be 40%, 38.5% and 37% for the three buildings respectively at the specific simulated energy demand conditions.

Performance and economic analysis of a heat pump water heater assisted regenerative solar still using latent heat storage

• Maximum hourly productivity of 1.73 kg/h was observed during peak sunshine hours. • Maximum productivity of 16.1 kg/day was observed during summer months. • Maximum and minimum daily efficiencies were estimated as 0.81 and 0.78, respectively. • Heat pump coefficient of performance was estimated in the range between 2.21 and 2.55. • Cost of distillate was estimated as 0.05471 USD per liter. • Payback period was estimated as 21.46 months for 275 days of operation. • CO 2 emissions is estimated as 26.8 tons for 275 days of operation in ten-year life. In this paper, the performance and economic analysis of a heat pump water heater assisted regenerative solar still using latent heat storage was experimentally studied. The heat released during condensation of water vapour inside the solar still was regenerated using a heat pump to pre-heat the saline water before entering the basin and producing hot water for domestic use. The experimental observations have been made under the influence of solar irradiation, ambient temperature and ambient wind speed. The results showed that the heat pump water heater assisted regenerative solar still has attained cumulative productivity between 14.0 and 16.0 kg/day during 12 h of operation. Moreover, the system delivers more than 100 L of hot water at 46.8–52.8 °C for domestic use. The monthly average coefficient of performance of the heat pump was estimated in the range between 2.21 and 2.55. The cost per litre of distillate obtained from the system is estimated as 0.05471 USD. The payback period was estimated as 21.46 months for 275 days of operation in a year at 12% interest and distillate selling price of 0.08 USD/litre. The water samples obtained from the experiments are suitable for drinking and met the Bureau of Indian Standard requirements.

Real Domestic Hot Water Consumption in Residential Buildings and Its Impact on Buildings’ Energy Performance—Case Study in Poland

A building’s energy consumption is assessed considering the energy required for heating, cooling, lighting, and domestic hot water (DHW). Methodologies used to calculate energy certificates in European Union countries consider hot water consumption rates per person or per heated (floor) area, giving wide-ranging values (35–88 dm3/person/day). Using extreme parameters, it is possible to obtain a primary energy index that meets the legal requirements, although unrealistically large proportions of domestic hot water use relative to the total energy balance of the building may marginalize the influence of other components, such as fluctuations in heating, ventilation, or lighting. In the current work, the DHW consumption of three residential buildings was measured to verify the energy consumption for hot water preparation. Investigations were conducted based on the consumption of natural gas for DHW preparation. Experimentally obtained water consumption rates were determined per m2 of a dwelling and per person living in the building. The calculated indicators (0.85 ± 0.005 dm3/m2/day and 27.4 ± 1.4 dm3/person/day) were lower than those used for energy certifications of buildings. The experimentally obtained indicators were used in further theoretical energy assessments of six residential buildings. By adopting the designated indicators, the analyzed buildings met the legally required primary energy value (<70 kWh/m2/year) when using natural gas as a heat source. Applying more realistic DHW consumption values resulted in more accurate energy certifications.

Development of a dynamic analytical model for estimating waste heat from domestic hot water systems

Domestic Hot Water (DHW) production accounts on average between 14 14% and 30% of the residential energy consumption worldwide. In UK dewllings, a quarter of the energy is consumed to produce hot water and this proportion is likely to increase as the energy required for space heating reduces over time in order to achieve demand reduction targets. As the margins for improving the performance of heating system technologies increase, the need for improving modelling accuracy and precision increases also. Although studies have considered DHW use in buildings, there is a lack of reflection on the energy loss and performance of systems in contemporary dwellings. Current simulation tools with simplified assumptions, limited variables and algorithm detail, might lead to unreliable results in terms of the estimated heat losses. In this research, an analytical dynamic model has been developed to estimate heat loss from a domestic hot water system based on high resolution monitored data for a set of dwellings in the UK. The model estimated heat losses during flowing and non-flowing (cooling down) conditions in the distribution system as well as heat losses from the storage tank. It was found that apart from the significant heat loss from the storage tank, short draw-offs are particularly influential in determining the amount of heat wasted. Considerable savings might be achieved ‘avoiding’ short draw-offs through a better control of the system and/or changes in the user behaviour. Insulating and reducing the effective length of the distribution pipe network through better design of the system similarly predict significant reductions in heat losses.

Analytical study on changes in domestic hot water use caused by COVID-19 pandemic

COVID-19 made considerable changes in the lifestyle of people, which have led to a rise in energy use in homes. So, this study investigated the relationship between COVID-19 and domestic hot water demands. For this purpose, a nondimensional and principal component analysis were conducted to find out the influencing factors using demand data before and after COVID-19 from our study site. Analysis showed that the COVID-19 outbreak affected the daily peak time and the amount of domestic hot water usage, the active case number of COVID-19 was a good indicator for correlating the changes in hot water demand and patterns. Based on this, a machine learning model with an artificial neural network was developed to predict hot water demand depending on the severity of COVID-19 and the relevant correlation was also derived. The model analysis showed that the increase in the number of active cases in the region affected the hot water demand increased at a certain rate and the maximum demand peak in morning during weekdays and weekends decreased. Furthermore, if the number of active cases reached more than 4000, the peak in morning moved to afternoon so that the energy use patterns of weekdays and weekends are assimilated.

Energy and flow demand analysis of domestic hot water in an apartment complex using a smart meter

This study aims to reveal new perspectives on domestic hot water use regarding seasonal behavior, peak demand deviation with temporal metering resolution and time averaging, initially discarded characteristics, and the possibility of demand forecasting based on the previous outdoor temperature. To determine the use of domestic hot water in 918 households located at the study site, new smart meters and peripheral systems were used to collect sub-meter readings of energy/flow/discarded hot water demands and endpoint temperatures every 30 s. Collected data on individual households can provide quantitative information about seasonal changes, variations in hot water service quality among end-users, and end-user behaviors of initial use. Furthermore, statistical and machine learning analyses indicate the possibility of forecasting seasonal domestic hot water consumption through an understanding of the relationship between energy demand and previous outdoor temperatures. Through a clear understanding and with a proper use of the demand behavior as it pertains to domestic hot water, guidelines can be derived to enable the efficient use of domestic hot water by households as well as the efficient operation by heat suppliers.

Techno-economic investigation of the potential for energy efficiency measures of multi-story apartment buildings at cluster level using different district heating tariffs

The paper presents a systematic approach to study energy renovation of a larger building stock at cluster level using categorization, classification into modules, life cycle cost (LCC) optimization, and bottom-up modeling. Seven-story clusters of slab and tower blocks were analyzed to investigate the techno-economic potential for energy efficiency measures (EEMs) of 401 multi-story apartment buildings constructed during the 1960s and ’70s located in two Swedish municipalities. Energy renovation with intention of lowest LCC and projected energy savings concerned the purchased energy for space heating and domestic hot water use allocated by different district heating (DH) tariffs. Different conditions for DH and performance indicators demonstrated code compliance. The LCC analysis revealed a rather small potential for cost-optimal EEMs on building envelopes. Incentives to gain LCC savings during energy renovation increased with a DH tariff with constant price rate throughout the year. A starting point for cost-effective energy renovations arguably would be clusters with 94 two-story and 160 three-story slab blocks, also requiring fewer EEMs to achieve thermal performance requirements. DH systems using industrial excess heat can improve buildings’ energy performance and reduce required EEM investment costs. The DH price level is shown to be a vital issue for cost-effective energy renovations.

Energy flexibility potential of domestic hot water systems in apartment buildings

Domestic Hot Water (DHW) storage tanks are identified as a main source of flexible energy use in buildings. As a basis for energy management in apartment buildings, this paper describes the aggregated DHW use in a case building, and analyses the potential for DHW energy flexibility by simulating different control options. The case study for the work is an apartment building in Oslo with 56 apartments and a shared DHW system. Energy measurements are available for consumed hot water, hot water circulation, and energy supplied to the DHW tanks. The measurements are presented with minute, hourly and daily values. Aggregated daily energy use for the consumed hot water is in average 362 kWh, while the energy supplied is 555 kWh. The potential for energy flexibility is analysed for a base case and for four different rule-based control options: Power limitation, Spot price savings, Flexibility sale and Solar energy. Economic consequences of the control options are compared. With the Norwegian tariff structure, maximum hourly power use has the main impact on the cost. Control systems that aim to reduce the maximum power use may be combined with spot price savings or to offer end-user flexibility services to the grid.