Energy Consumption In Houses Research Articles

A simplified method for calculating air conditioning load of multi-family housing community considering the spatiotemporal distribution of occupants

Occupant behavior is considered a vital factor influencing housing energy consumption by adjusting the indoor thermal environment and the use of service system. Multi-family housing is popular in the hot summer and cold winter (HSCW) zone of China, where residents typically adopt a “partial time, partial space” approach to environmental control. Therefore, the behavioral differences among families pose challenges for predicting hourly energy consumption at community scale. Taking Changsha, a typical city in the HSCW zone, as an example, this study investigates and summarizes the spatiotemporal distribution characteristics of multi-family occupants, analyzing their impact on air conditioning (AC) load under ideal environmental control conditions (i.e., AC is on where is occupied). Hourly AC load modified equations and multi-family occupancy modified coefficients are introduced to construct a method for estimating hourly AC load of residential communities that comprehensively considers the spatiotemporal distribution of multi-family occupants. The results show that the proposed method achieves fitting degrees of 87%-92.4% for predicting heating load and 77.9%-84.9% for predicting cooling load. This method not only dramatically reduces the workload and time required for physical modeling of residential communities, but also enables fast and refined simulation of the hourly AC load.

Optimizing the Integration of Building Materials, Energy Consumption, and Economic Factors in Rural Houses of Cold Regions: A Pathway

Limited material options and economic conditions significantly restrict the potential for energy efficiency improvements in rural houses in China’s cold regions. It is worth exploring how to propose suitable energy-saving renovation plans for rural houses in cold regions under practical constraints. By using Grasshopper within Rhinoceros 8 software, an algorithm integrates material selection, energy consumption calculations, and economic analysis. The method efficiently generates thermal optimization schemes, providing insights into energy use, costs, and payback periods. In a case study of a typical rural house in Daqing City, the optimized scheme achieved over 70% energy savings compared to traditional homes, with renovation costs amounting to less than 40% of residents’ annual income and a 2-year payback period. This significant improvement highlights the potential of the proposed method in enhancing the energy efficiency and economic viability of rural house renovations.

Impact of windcatcher on the thermal comfort and indoor air quality in a modern house

ABSTRACT As the residential construction sector grows more quickly than other building sectors in Oman and other GCC nations, the proportion of total energy consumption in housing rises. The native inhabitants have implemented various strategies to enhance the thermal conditions in structures. The windcatcher was one of those methods used in hot climates when it was vital to achieve thermal comfort. Computational Fluid Dynamics (CFD) is used in this research to find the best orientation of a windcatcher attached to a modern house in Muscat. In addition, thermal comfort and indoor air quality based on CO2 concentration were also studied. This paper tries to fill the gap in research on using windcatchers with modern houses and find the best design for such windcatchers. Results indicate that placing a windcatcher with a rotation of 30° from the north towards the east will give better air velocity than a windcatcher with angles of 0°, 45°, and 60°. Furthermore, it was found that the windcatcher improved the Predicted Mean Vote (PMV) by 34% and the Predicted Percentage of Dissatisfaction (PPD) by 48%. In addition, a modern house with a windcatcher reduced CO2 concentration by more than 5% compared to a house without a windcatcher.

Optimization of energy consumption in residential housing within the framework of energy sustainability strategies. A case study in the Canary Islands

Given that the energy consumption of buildings equates to practically 40 % of the total energy demand of any electrical system, research studies on reducing such energy consumption are of paramount importance. This problem is accentuated in isolated weak electrical systems, like those found in many island territories, where there are significant limitations on the management of energy generation and demand. The objective of the present work is to identify the relationship between the energy consumption of residential housing and its thermal envelope characteristics within the framework of energy sustainability strategies. For this purpose, multiple solutions were compared based on combinations of values associated to different types of residential building, a wide range of thermal transmittance values based on the application of different materials and/or envelope designs, and different climate zones. It is concluded that the energy consumption of these buildings can be significantly reduced by optimizing the technical characteristics of the thermal envelope, adapting it the surrounding climate conditions. For the case study, the specific energy saving can be as high as 51.4 kWh/m2·year. Bearing in mind the housing stock in the study area, the total energy saving is equivalent to 9.17 % of its current energy demand. The results and conclusions obtained can be used in the development of planning strategies for new housing and/or the optimization of energy consumption in already existing housing stock.

Quantum support vector machine for forecasting house energy consumption: a comparative study with deep learning models

The Smart Grid operates autonomously, facilitating the smooth integration of diverse power generation sources into the grid, thereby ensuring a continuous, reliable, and high-quality supply of electricity to end users. One key focus within the realm of smart grid applications is the Home Energy Management System (HEMS), which holds significant importance given the fluctuating availability of generation and the dynamic nature of loading conditions. This paper presents an overview of HEMS and the methodologies utilized for load forecasting. It introduces a novel approach employing Quantum Support Vector Machine (QSVM) for predicting periodic power consumption, leveraging the AMPD2 dataset. In the establishment of a microgrid, various factors such as energy consumption patterns of household appliances, solar irradiance, and overall load are taken into account in dataset creation. In the realm of load forecasting in Home Energy Management Systems (HEMS), the Quantum Support Vector Machine (QSVM) stands out from other methods due to its unique approach and capabilities. Unlike traditional forecasting methods, QSVM leverages quantum computing principles to handle complex and nonlinear electricity consumption patterns. QSVM demonstrates superior accuracy by effectively capturing intricate relationships within the data, leading to more precise predictions. Its ability to adapt to diverse datasets and produce significantly low error values, such as RMSE and MAE, showcases its efficiency in forecasting electricity load consumption in smart grids. Moreover, the QSVM model’s exceptional flexibility and performance, as evidenced by achieving an accuracy of 97.3% on challenging datasets like AMpds2, highlight its distinctive edge over conventional forecasting techniques, making it a promising solution for enhancing forecasting accuracy in HEMS.The article provides a brief summary of HEMS and load forecasting techniques, demonstrating and comparing them with deep learning models to showcase the efficacy of the proposed algorithms.

Social welfare maximization with efficient energy management of community microgrid considering customer behavioral response using MDCLPIS

A novel residential demand response (DR) program is proposed to encourage microgrid customers to participate in demand reduction using a house energy consumption scheduler (HECS) to maximize social welfare by optimally reducing the cost of electricity and to receive maximum incentives. In this research, both Price-based and incentive-based DR programs are considered, and the demand-price elasticity approach is implemented to model them. To make the DR model more effective, customer behavioral response is taken into account, by leveling the customers according to their price elasticity. An optimized non-cooperative energy game theory framework and a Nash equilibrium strategy are employed to model customer response under a multi-time of use (TOU) pricing and incentive scheme, with the aim of achieving the best scheduling of home-controlled appliances while preserving customers’ privacy. This approach maximizes social welfare by minimizing the electricity consumption cost for customers while simultaneously saving on generation costs. The negative externality effect on price rate is internalized between the customers of different consumption levels using multi-dynamic consumption-level pricing and incentive scheme (MDCLPIS). The suggested model distinguishes between consumers’ behavior in response to changes in energy prices and their behavior in response to changes in incentives. Finally, numerical and simulation results demonstrate the suitability and effectiveness of the proposed model and its advantages in terms of customer saved cost by up to 21.26%, microgrid saved cost by up to 29.44%, and getting benefits by optimal scheduling of their load.

Multi-Objective Optimization for Winter Heating Retrofit in Rural Houses of Cold Regions: A Case Study in the Wusu Area

In regions of China experiencing severe cold, the duration of the winter heating season significantly contributes to elevated heating energy consumption in rural dwellings. This study focuses on typical brick-and-concrete rural homes in the Wusu area. Utilizing the Rhino–Grasshopper parametric modeling platform, it aims to minimize heating-related carbon emissions and the overall costs associated with retrofitting. The approach involves improving the insulation properties of the building envelope to reduce energy requirements. Additionally, the study incorporates solar photovoltaic systems atop rural homes, building upon low-carbon, passive, energy-efficient design principles. By examining the influence of various factors on rural housing energy consumption, the research employs the entropy weight method to identify the most effective design solutions. The goal is to explore strategies for the energy-efficient retrofitting of rural dwellings in areas faced with harsh winter conditions, aligning with the objectives and preferences of Applied Sciences. The simulation results reveal the following: (1). In comparison with the baseline scenario, 42.2% of the optimized solutions within the Pareto frontier satisfy the current standards for 75% energy savings in energy-efficient residential design. (2). The lowest recorded thermal consumption index for the buildings can reach 12.427 W/m2, at which point the rate of energy savings is elevated to 79.5%. (3). Within the solutions identified by the Pareto frontier, 80% exhibit initial investments that are lower than the cost savings over the lifecycle due to reduced energy consumption (dCg < 0), demonstrating the economic feasibility of the proposed retrofitting strategies.

Increasing single households challenges household decarbonization in Japan

In light of societal shifts such as an aging population, delayed marriages, and higher rates of divorce, there's a notable rise in solitary living, affecting society, the economy, and the environment. To understand the implications of these demographic shifts, our research examines the nexus between solo living and its broader social-environmental consequences. Using Japan, one of the countries with the highest proportion of the elderly, as a reference, we explore the temporal fluctuations, gender-specific variances, and long-term trends in carbon footprints influenced by alterations in consumption behaviors. Results indicate that housing energy and food consumption remain the dominant carbon footprint contributors across all demographic sectors. Interestingly, single households present higher carbon footprints than non-single households, with those of single females surpassing their male counterparts due to increased household energy use and expenditures on clothing and healthcare. Following the demographic shifts, single households are expected to account for approximately 31.1% of Japan's emissions from households by 2040, challenging national decarbonization efforts due to their higher per capita emissions. This highlights the imperative for bespoke strategies, especially in resource allocation and sharing, to address the solo living challenge and ensure congruence with Japan's sustainability and decarbonization goals.

Energy consumption and environmental parameters in Madrid social housing. Performance in the face of extreme weather events

Housing energy rehabilitation is currently a priority in Europe. In vulnerable neighbourhoods, improving energy efficiency and comfort conditions has an important impact on people living conditions. Rehabilitation strategies are usually designed according to standard scenarios, without taking into account variations related to the users or the type of building. Monitoring this type of housing offers valuable information to be able to select the best strategies based on a diagnosis of their real performance.In this paper we present one year data on energy consumption, hygrothermal comfort and CO2 monitored for 22 dwellings in Madrid, located in six different vulnerable locations. In addition to normal periods, the monitoring campaign includes periods of extreme heat and cold, and two exceptional situations, the “Filomena" snowfall and the COVID pandemic period.The results report poor indoor environmental quality in these dwellings, both in the main rooms and in the bedrooms. Also poor building quality is observed. The recorded energy consumption is lower than that given as a reference in the standards or models, at the cost of a worse indoor environment. The analisys supports the importance of retrofit strategies that take into account the composition of the household, such as single-person households or those with children, the higher vulnerability of dwellings with higher demand, the subjective perception and the role of users, and the need to incorporate improvements in indoor air renewal.

Greenhouse Gas Payback Time of Different HVAC Systems in the Renovation of Nordic District-Heated Multifamily Buildings Considering Future Energy Production Scenarios

The European Union (EU) has implemented several policies to enhance energy efficiency. Among these policies is the objective of achieving energy-efficient renovations in at least 3% of EU buildings annually. The primary aim of this study was to offer a precise environmental comparison among four similar district-heated multifamily buildings that have undergone identical energy efficiency measures. The key distinguishing factor among them lies in the HVAC systems installed. The chosen systems were as follows: (1) exhaust ventilation with air pressure control; (2) mechanical ventilation with heat recovery; (3) exhaust ventilation with an exhaust air heat pump; and (4) exhaust ventilation with an exhaust air heat pump with a Photovoltaic (PV) panel. This study involved a life cycle assessment that relied on actual material data from the housing company and energy consumption measurements. This study covered a period of 50 years for thorough analysis. A sensitivity analysis was also conducted to account for various future scenarios of energy production. The findings revealed that the building with an exhaust air heat pump exhibited the lowest greenhouse gas emissions and the shortest carbon payback period (GBPT), needing only around 7 years. In contrast, the building with exhaust ventilation without heat recovery showed the highest emissions and the longest carbon payback period (GBPT), requiring approximately 11 years. Notably, the results were significantly influenced by future scenarios of energy production, emphasizing the crucial role of emission factors in determining the environmental performance of distinct renovation scenarios.

Measurement of Dynamic Electric Consumption Trend in a Broiler House in Arkansas

Highlights We quantified the diurnal and seasonal electric load pattern during four consecutive growouts of heavy broilers over an 11-month period on a commercial farm. Consumption was measured separately for ventilation fans, lights, feed motors, well pump motors and radiant tube heater blowers. Ventilation fans were the largest consumer of energy and the largest contributor to peak demand on the farm. Average monthly electric demand ranged from 31% to 77% of the corresponding monthly peak demand, calculated from the largest 15-min period. The electricity cost of raising heavier-weight broiler chickens is higher in the summer ($22 per 1,000 kg live weight) than in the winter ($5 per 1,000 kg live weight). Abstract. Characterizing electric energy use in a commercial broiler house—partitioned by bird age, season, and electric load type—can provide engineers with insight for designing energy conservation practices, as well as help to analyze and design on-site renewable energy production sub-systems. This study monitored electric energy consumption in a commercial broiler house in Arkansas, raising heavy broilers (with a capacity of 17,000 birds) during an 11-month period that included four, 8-week flocks. Average electricity use (RMS voltage and current) was measured at a sampling interval of 5 min for all of the major load types, including ventilation, lighting, feed motors, and well-water pump motor. Results indicated that electric consumption for ventilation fans was the greatest load, accounting for over 88% of the total. The endwall fans (for tunnel ventilation) contributed to the peak electric demand (VA) in nine of the 11 months. Over the 11-month monitoring period, broiler chicken production had an average electric energy consumption (per unit live-weight mass produced) of 43 kWh per metric ton of live weight (in a winter flock) to 184 kWh per metric ton (in a summer flock). The corresponding electric costs were $5 per metric ton and $22 per metric ton in winter and summer flocks, respectively (based upon an electricity unit cost of $0.12/kWh). Keywords: Electricity demand, Energy efficiency, Poultry housing.

Analysis Based on Ecotect Software of the Energy Consumption and Economic Viability of Solar Collector Pig Houses with Different Roof Forms and Translucent Materials

This study investigated improvements in solar energy efficiency in pig houses with different roof types and translucent materials using Ecotect simulation software v2011. An experimental pig house in Qian Guo County, Songyuan City, was selected as the research object. First, the optimal building orientation for pig houses was explored by analysing the solar irradiance received as a primary indicator. Next, the energy consumption of pig houses was simulated using various translucent materials in the roof. Various roof types were designed based on the optimal translucent material. The energy consumption of these designs was compared with that of the experimental pig house. Finally, the economic viability of the different pig houses was analysed. The results showed that the optimal building orientation for a pig house was south by west 25°. Among the pig houses with equal-slope roofs using FRP daylighting boards, double-layer polycarbonate (PC) sheets, flat glass, and organic glass as translucent materials, the pig house using double-layer PC sheets required the least amount of additional heat during the heating season (62,109 kWh). For pig houses with double-layer PC sheets, four roof designs were assessed (equal-slope roof, front and rear roofs with unequal slopes, upper and lower staggered unequal-slope roof, and four-equal-slope roof), and it was found that the pig house with a four-equal-slope roof required the least amount of additional heat during the heating season (48,138 kWh). The economic analysis indicates that the combination of a four-equal-slope roof and double-layer PC sheets is the most cost-effective option, with the lowest total life cycle cost. This design saves 9521 USD compared with the experimental pig housing, effectively improving solar energy utilisation efficiency. These analyses provide a reference for exploring the thermal performance of pig houses.

Investigation of Energy Consumption via an Equivalent Thermal Resistance-Capacitance Model for a Northern Rural Residence

To achieve the goal of carbon peaking, it is crucial to reduce both carbon emissions and energy consumption during the operational stage of residential buildings. This paper proposed a method for assessing carbon emissions and energy consumption for an energy system utilized in a rural residence. First, an equivalent thermal resistance-capacitance model for a rural residence was established. The parameters of thermal resistance and capacitance were optimized based on the data collected from an operating air source heat pump heating system. On this basis, the energy consumption was derived, and it was compared with real consumption. Then, a method of estimating house energy consumption index per unit area under specified weather conditions was proposed. Finally, the carbon emissions of three heating types—heating driven by air source heat pump, gas boiler, and coal boiler—were compared. Results showed that the derived energy consumption index per unit area was 46.77 W/m2. The carbon emissions of the air source heat pump were 1406.1 kgCO2.

Computational Review and Assessment of The Urban Heat Island Effect and Its Impact on Building Space Conditioning

This paper reviews and reports the recent progress and knowledge on the specific impact of the urban heat island (UHI) effect on building space conditioning for vulnerable housing where lack of air conditioning and fuel poverty causes indoor overheating, thus increasing vulnerability. Previous studies demonstrated that the increase of the ambient temperature due to UHI and heat waves impacts adversely cooling energy consumption of buildings and raises the peak electricity demand during summer and heat waves. Given the aging and dilapidated housing conditions in low-income communities, mostly of color and minorities, the economic burden of the cooling energy penalty induced by urban overheating is higher. However, literature on overheating is primarily driven by the physical characteristics of the building such as insulation, albedo, and envelope properties, and the Heat Vulnerability Index (HVI) by demographic data such as age, income, education largely remains isolated thus failing to capture the overall understanding of heat vulnerability and the role architects/urban designers can play in mitigation. Through a computational query review of the last fifteen years of publication, we are inquiring, about how UHI impacts building energy consumption in low-income and poor-quality housing and what role city and housing characteristics play in indoor overheating. Our study suggests, that in the US, due to segregated historic planning policies, low-income houses are often located in low tree canopy areas with varying urban typologies, and higher impervious material which substantially increases the air temperature thus determining energy consumption and anthropogenic heat release which contribute to present-day inequitable exposure to intra-urban heat. Both housing characteristics and the location of housing play a crucial role as similar housing will experience different exposure to intra-urban heat if not located in a heat canyon. Through this literature review, it became evident that there is a gap in the research that fails to connect building characteristics and overheating with heat vulnerability. Research involving UHI and heat vulnerability has continued to advance through energy analysis and mitigation studies, but future studies need to redefine the HVI index, especially by incorporating city and housing characteristics, which can help architects/urban designers make informed design decisions.

Modelling of multi-energy systems of residential buildings with Calliope and validation of results

Given the current and future importance of energy availability in the residential sector, there is great interest in reducing and optimising the energy consumption of single and multi-family houses. An energy system consisting of solar PV, heat pump, and battery and thermal storage can reduce electricity grid dependence by maximising the use of renewable energy. However, creating a model for such a multi-energy system can be challenging due to the need for detailed input data and high computational cost. This study aimed to develop a simpler model with short computation times and sufficient accuracy by employing a mixed-integer linear programming (MILP) method based on the modelling framework Calliope, allowing the investigation of optimal operation regarding energy consumption and autarky. The results of a model simulation were compared to measurements acquired at a pilot site of a residential building in an alpine region of Switzerland. The results show good agreement between the model and pilot site for PV self-consumption (-5%), grid-export (+3%), and battery usage (-2%), whereas larger discrepancies were observed for the utilisation of the thermal storage (-78%). With the necessary modifications the developed model will be useful in estimating the feasibility and impact of multi-energy systems in residential buildings.

Built environment, daily activities and carbon emissions: Insights from an eight-week app-based survey in the Province of Utrecht (Netherlands)

The built environment affects our lifestyles and associated carbon emissions. Most studies investigating this link, however, have used census data or surveys that can hardly capture patterns of daily activities over time. In this paper, we present the results of an eight-week monitoring campaign during which over 300 residents of the Province of Utrecht (Netherlands) recorded their activities pertaining to the domains of travel, housing energy and food consumption using a mobile app. Data were analyzed through structural equation models (SEMs) accounting for residential self-selection.Once controlling for socio-demographics, an aggregate measure of compactness encompassing population density, intensity of development and accessibility of transit and grocery stores was found to be associated with emissions in all three domains via multiple mediating variables. More compact neighborhoods may curb emissions by reducing car ownership and therefore driving, but seem to have no impact on other travel habits. Yet, they tend to be associated with residential settings (i.e. older multi-dwelling units) showing higher energy emission factors due to less reliance on green sources. Additionally, lower rates of car ownership in more compact neighborhoods are associated with less carbon-intensive diets. While our findings endorse the compact city as an instrument to help cut individual carbon emissions, they also raise warnings about the ability of low-carbon technologies (e.g. electric vehicles, photovoltaic systems) to make this spatial planning model look obsolete in the fight against climate change. Mobile apps like ours may help disentangle this puzzle though greater automation is needed to get unbiased information.

Indoor Lighting Study: case of UDES’ Solar Smart House Bou-Ismail-Algeria

In the residential sector, the energy consumed for lighting represents an important amount of the total energy consumption in a typical house. In this paper, a lighting system in a solar smart house prototype in Bou-Ismail, Algeria, has been studied. This study focuses on the optimization of lighting and the energy consumption in the house, where the experimental and the simulation of the light distribution in the house was carried out using DIALux software. The purpose is to find the most suitable devices that are able to reduce lighting energy consumption and still ensure an optimal visual comfort inside the house. The obtained results show the efficiency of the proposed lighting system, where a reduction in energy consumption of 55.87% has been achieved.

Experimental Study of the Dimensional and Hygrothermal Properties of Hemp Concrete under Accelerated Aging

In this article, the functional properties of hemp concrete are studied. Hemp concrete stands to reduce the carbon impact and improve the energy consumption of houses. Hence, numerous properties are measured: mass and dimension (volume) variations are found, as is the variability in hygrothermal properties (density, thermal conductivity, heat capacity, moisture buffer value, and water vapor permeability). This entry proposes three different characterization campaigns. The first is a short introduction to the spatial variability in thermal conductivity; the second is dedicated to the study of univariate variations in the mass, volume, and hygrothermal properties of hemp concrete samples. The last one tackles the aging evolution of the properties characterized during the second campaign, in which the samples follow several aging protocols, including exposure to outdoor conditions, the application of immersion-drying cycles, and the application of freeze–thaw cycles. A set of samples is kept under control conditions to allow for comparison. As the main result, spatial variability was found in the material. This is related to the random manufacturing variability or the spatial position regarding the height of the manufactured element. A high univariate variability is found across hemp concrete samples. Moreover, the storage of samples under stable reference conditions implies very little change in the studied materials’ properties, whereas all accelerated aging protocols implied major changes of properties. In particular, we observed an evolution of the thermal conductivity of the samples kept under control conditions for 4 months, with the thermal conductivity ranging from −2.7% to +6.3% with a mean evolution of +1.22%. We observed an increase in the same property, ranging from +2.7% to +18.3%, with a mean of +9.0% for samples kept for 4 months under natural outdoor conditions, an increase ranging from +7.3% to +23.6% with a mean of +15.2% for samples that had undergone 20 cycles of immersion-drying, and an evolution of this property ranging from −5.6% to +12.3% for samples that had undergone 20 freeze–thaw cycles.

Discussion on the Influence of Window-wall Comparison on Energy Consumption and Natural Lighting of Traditional Houses

Discussion on the Influence of Window-wall Comparison on Energy Consumption and Natural Lighting of Traditional Houses

Structural Characteristics of the Household Carbon Footprint in an Aging Society

The aging population has posed a challenge to China’s carbon neutrality pledge. To study the household carbon footprint in an aging society, this paper has combined the age-specific consumption pattern and environmental input-output life cycle assessment (EIO-LCA) to calculate the carbon footprint of household consumption across age groups, and then identified the key pathways of carbon emissions via structural path analysis (SPA). Results indicate that the elderly contribute 11.65% to total consumption-based carbon emissions. The working group (ages 15–64) has the highest average carbon footprint (0.85 tCO2e), while the elderly group (ages 65 and above) has the lowest average carbon footprint (0.82 tCO2e). Urban households of all ages have a higher carbon footprint than rural households. Housing and food are the dominant sources of the elderly carbon footprint. Notably, the production and distribution of electric power and heat power sector associated with housing energy consumption plays a leading role in the carbon emissions pathways of elderly consumption. Measuring the carbon footprint of older people can support policy designs and decision making in key sectors along the supply chain, and further encourage low-carbon lifestyles among China’s elderly. Additionally, the findings of this study have broad applications, especially for developing countries undergoing demographic transitions.