Residential Consumption Research Articles

Understanding the environmental impacts of residential water consumption in Brazil: Integrating the building stock with life cycle assessment

Understanding the environmental impacts of residential water consumption in Brazil: Integrating the building stock with life cycle assessment

Forecasting Natural Gas Consumption by User Type Using Machine Learning: A Comparative Study

This study aims to develop user-type-specific machine learning models to forecast natural gas consumption for residential and commercial user groups in İzmir, Turkey. Multiple Linear Regression, Random Forest, LightGBM, and XGBoost algorithms were implemented, and model performance was enhanced through hyperparameter optimization. The models were evaluated using MAE and RMSE metrics. Results indicate that LightGBM and Random Forest provided the most accurate forecasts, while MLR underperformed due to the non-linear nature of the data. Residential consumption patterns were found to be more predictable, leading to lower error rates, whereas the commercial group exhibited higher variability and forecast challenges. The study highlights the importance of distinguishing user types and employing well-tuned machine learning algorithms for improved energy demand forecasting.

Disaggregation of Hourly Electricity Consumption into Subconsumptions

Electricity consumption in a country, region, or city comprises demand from residential, industrial, commercial, and other specific consumptions such as street lighting, and agricultural related consumptions, along with losses from leakage and transmission and unauthorized usage. However, tracking the specific consumption components in real time is challenging, as detailed consumption data for each category is typically determined only after billing periods. Understanding the electricity demand of each subcategory and its proportion within total consumption is crucial for effective system planning and operation. This study develops a framework to disaggregate total electricity consumption into subcategories—residential, industrial/ commercial, irrigation, and lighting—at an hourly resolution. The methodology depends crucially on a special day detection approach that identifies consumption patterns on days at which where electricity usage is at its lowest, without any a priori information of the holidays in each country or a region. Based on this, baseline consumption levels for different periods are determined, indices for subcategories are developed, and time series models are generated to analyze consumption trends. For validation, the methodology is applied to Turkish electricity data, where the structure of consumption is analyzed in relation to holidays and special occasions, accounting for the reduction in demand during these periods. The proportions of residential, industrial/commercial, and lighting consumption are derived from total consumption, and the remaining categories are estimated accordingly. Finally, the accuracy and convergence of the results are evaluated, and the findings are presented with their implications for energy planning and grid management.

Residential low-carbon consumption behavior: insight from social cognition theory

PurposeThe international community is currently focused on reducing carbon emissions and coping with climate change. Encouraging residents to adopt a wider range of low-carbon consumption behaviors will help achieve carbon reduction targets and alleviate global climate change.Design/methodology/approachWorking from social cognitive theory, this paper uses questionnaire data from 657 Chinese residents, collected from March 29 to May 29, 2024, to establish a structural equation model to study the influencing factors and driving mechanisms of residents’ low-carbon consumption behaviors.FindingsThis study finds that (1) of the individual factors, low-carbon cognition and self-efficacy positively impact low-carbon consumption behavior. (2) Of the environmental factors, group pressure and media publicity positively impact low-carbon consumption behavior. (3) Of the environmental factors, group pressure, media publicity and policies and regulations all positively impact low-carbon consumption behavior through the mediation of low-carbon cognition.Research limitations/implicationsThis study contributes to the growing body of literature on low-carbon consumption behavior, demonstrating the application of social cognitive theory in exploring the drivers of behavior.Practical implicationsStrategies for the government to promote residents’ low-carbon consumption behavior are proposed, which will help the government achieve its carbon reduction goals.Originality/valueThis paper uses social cognitive theory to explore the driving factors of residents’ low-carbon consumption behavior. Prior studies have only considered psychological factors; this study includes environmental factors, given their known influence on individual behavior and the interactions among environment, individual and behavior, to clarify the paths of their mutual influence.

Combined intervention: Structural-efficiency-intensity indicators can halve nitrogen-related resource-environmental effects by 2050 in Beijing.

Combined intervention: Structural-efficiency-intensity indicators can halve nitrogen-related resource-environmental effects by 2050 in Beijing.

Patterns and Predictors of Residential Indoor Water Use Across Major US Cities

Abstract This study investigates residential indoor water consumption variability across 39 US cities using data from 26,441 single‐family smart water meters. Employing functional data analysis and mixed‐effects random forest, we identified distinct usage patterns across city clusters, with 13 high and 6 low water‐using cities (all in coastal California) differing significantly from 20 medium water‐using cities. Shower and toilet use were primary drivers of indoor use differences between clusters, influenced by both behavioral and fixture efficiency factors. The presence of appliances, certain household features, and weather also affect indoor water use, with varying influence on indoor water use across clusters. Our findings highlight the effectiveness of state‐level water efficiency interventions and emphasize the importance of considering both behavioral factors and appliance efficiency in conservation strategies, providing valuable insights for targeted water demand management in urban areas.

Carbon Emission and Scenario Prediction of Water System in Zhejiang Based on the Whole Life Cycle of Water Resources

Clarifying the "water-energy-carbon" nexus process and variation in the carbon emissions of a water system throughout the lifecycle of water resources is crucial for regional water resource management, energy-efficient utilization, and low-carbon development. This study introduces a comprehensive analytical framework for assessing carbon emissions across the entire lifecycle of water resources, grounded in the "water-energy-carbon" nexus. Utilizing statistical data from 2011 to 2021, the research analyzed the dynamic changes in carbon emissions in the water system in Zhejiang. Additionally, the STIRPAT model was employed to forecast carbon emissions from 2022 to 2040. The results showed that： ① The carbon emissions of the water system in Zhejiang mainly exhibited an "upward-downward-upward" trend, with an increase of 2.687 7 million tons in 2011-2012 and 4.888 4 million tons in 2020-2021, respectively, and a decrease of 11.371 6 million tons from 2012 to 2020. ② The carbon emissions of the water system in Zhejiang accounted for more than 95%, which had a decisive impact on the total change in the carbon emissions of the water system. ③ Urbanization rate was a key driving factor for changes in carbon emissions across various water system sectors, while population primarily affected carbon emissions from industrial and residential domestic water use. ④ The carbon emissions from the water system were at the lowest level under the low-carbon scenario and at the highest level under the extensive or coarse development scenario. Residential and public facility water consumption will be the main source of carbon emissions in the water system in the Zhejiang Province. Therefore, while controlling population growth and promoting urbanization, carrying out water-saving and emission reduction measures, including improving water use efficiency, optimizing the structure of water use, and reducing carbon emission intensity are necessary to effectively promote carbon reduction in the water system.

Estimating The Socioeconomic Factors Associated With Carbon Emissions at The Household Level For a Sustainable Future In Pakistan. A Case Study of Urban And Peri-Urban Areas of Faisalabad

Ever-accelerated urbanization and climate change pose significant challenges for sustainability especially in Pakistan. This study examines the social and economic features associated with the emission of CO2 from the household sector in Pakistan. This research concept constructed on the questionnaire and interview-based survey of 280 household respondents from seven major urban and peri-urban areas to estimate carbon emission from residential consumption from Faisalabad city of Pakistan in 2024 through conducting a survey and for carbon metric tons calculation, the webleading calculator for carbon emission has been used. Carbon emissions in urban areas from the primary sources of household are 0.99 metric tons in urban areas and 0.23 metric tons in peri urban from electricity, Gas, and oil burning, 3.29 and 3.10 from transport carbon emissions respectively in urban and peri-urban areas. Secondary Carbon emission sources contribute 2.520 metric tons in urban areas and 2.02 in other areas. These results indicate that socio-economic features (Income, house size, family size, and electricity bills) are the main contributors as Overall carbon emission is 3.98 metric tons from urban areas and 3.28 in peri urban area that represents 2/3 of carbon emission in the atmosphere, showing the scarcity of low carbon emission policies in the city. These verdicts highlight emission of carbon due to household’s activties poses serious challenges in achieving the SDGs goals for the green economy and society. Formulating custom-made strategies for areas and household usage is compulsory to minimize the issue and accomplish towards sustainable future for Pakistan.

Solar PV Generation and Consumption Dataset of an Estonian Residential Dwelling

Reliable data on residential power generation and consumption is vital for effectively integrating renewable energy sources. This is particularly important in the Baltic countries, where climate variability significantly impacts energy production and consumption. Such high-resolution residential usage data is beneficial for various applications, including planning, demand response, consumption behavior analysis, and forecasting. The dataset presented in this study contains one year (2023) of photovoltaic (PV) generation and energy meter power flow data collected at ten-second intervals from a residential dwelling in Estonia. To gather this data, two Camille Bauer PQ1000 power quality monitoring units were installed on the PV and meter side wiring of the house. The paper thoroughly discusses the data collection process, the original dataset, the processed data, and the feature analysis.

Quantifying Urban Carbon Footprints: Analyzing the Impact of Residential Lifestyles in Shah Alam, Selangor

In recent decades, there have been significant changes in the residents' lifestyle and the composition of their consumption. Consumption in urban households is increasingly important, contributing to an increase in carbon emissions. This study employed questionnaires to analyze CO2 emissions based on the daily activities of urban households and to estimate the total CO2 emissions per household in Shah Alam. A web-based carbon footprint calculator was used to determine the amount of carbon emissions produced as a result of direct and indirect residential consumption, which included the household size, income, daily commuting and transportation means, food, clothing, electricity, and household facilities. All data were then analysed and the factors that affected those emissions were investigated. The total amount of carbon emissions to the environment was estimated as 1,198.88 tonnes of CO2 per year. Key factors influencing this carbon footprint included the demographic information of the household, the daily food consumed, the primary spending items, energy consumption, the daily commuting, and the residents' environmental knowledge.

Intelligent system for management and optimization of residential water consumption

The project involves the development of an intelligent system for managing and optimizing water consumption in residential settings, addressing the significant loss of this vital resource due to leaks in domestic pipelines, particularly in toilets. To mitigate the issue of silent water leaks in toilets, a mechanism based on Hall effect sensors was implemented, allowing real-time monitoring of water flow in the toilet tank, detection of irregularities, and user notification through a real-time communication network. The designed system integrates a PIC18F4550 microcontroller, YF-B10 flow sensors, and a solenoid valve, all interconnected to a cloud database and linked to a graphical interface accessible from mobile devices. To evaluate its performance, water flow tests were conducted, obtaining data on the relationship between the pulses generated by the sensor and the actual flow rate. Through machine learning models, anomalous consumption patterns were identified, establishing a reliable method for early leak detection. Preliminary results demonstrate that the system can significantly reduce water wastage by promptly alerting users to potential leaks, thereby contributing to water conservation and environmental sustainability.

The Transition to an Eco-Friendly City as a First Step Toward Climate Neutrality with Green Hydrogen

A city of the future will need to be eco-friendly while meeting general social and economic requirements. Hydrogen-based technologies provide solutions for initially limiting CO2 emissions, with prospects indicating complete decarbonization in the future. Cities will need to adopt and integrate these technologies to avoid a gap between the development of hydrogen production and its urban application. Achievable results are analyzed by injecting hydrogen into the urban methane gas network, initially in small proportions, but gradually increasing over time. This paper also presents a numerical application pertaining to the city of Bucharest, Romania—a metropolis with a population of 2.1 million inhabitants. Although the use of fuel cells is less advantageous for urban transport compared to electric battery-based solutions, the heat generated by hydrogen-based technologies, such as fuel cells, can be efficiently utilized for residential heating. However, storage solutions are required for residential consumption, separate from that of urban transport, along with advancements in electric transport using existing batteries, which necessitate a detailed economic assessment. For electricity generation, including cogeneration, gas turbines have proven to be the most suitable solution. Based on the analyzed data, the paper synthesizes the opportunities offered by hydrogen-based technologies for a city of the future.

Toward Sustainable Urban Development: Exploring the Polycentric Transition of Chinese Cities Through Nighttime Lights

Studying urban spatial structure (USS) is crucial for advancing sustainable urban development. This study examined the USS of 292 cities in China from 2007 to 2022 using nighttime lights and the Herfindahl–Hirschman Index. The determinants of USS were analyzed through a fixed-effects spatial Durbin model based on the theoretical framework of influencing factors. The findings revealed a nationwide trend toward a polycentric USS across various city types. Economic growth drove the transformation from a monocentric to a polycentric USS. Additionally, the development of postal infrastructure promoted a shift toward a polycentric USS. Transportation infrastructure, industrial structure, residential consumption level, and government intervention were pivotal in shaping a monocentric USS. Regarding spatial spillover effects, transportation infrastructure, industrial structure, and economic growth fostered a polycentric tendency in neighboring cities, whereas government intervention reinforced a monocentric tendency.

Theoretical Analysis and Framework Design of Carbon Inclusion Mechanisms from the Perspective of Carbon Emission Trading Markets

According to the Global Carbon Emissions Report 2023, China's carbon dioxide emissions are about 12.7 billion tons, accounting for 34% of global carbon dioxide emissions, ranking first in the world. With China's rapid socio-economic development and growing population, there exists a huge potential for carbon emission reduction in the field of residential consumption. As an innovative voluntary emission reduction mechanism for individual low-carbon living and consumption in China, the Carbon Price Reduction System (CPRS) helps to encourage individual emission reduction behaviors, thus promoting carbon emission reduction. From the perspective of theoretical analysis and comparative case study, this paper analyzes in depth the operation logic of the carbon preference system, combs through the current status of carbon preference system practice and compares it, and puts forward the design of the carbon preference system framework which is led by the government, driven by the enterprises, promoted by the financial institutions, and participated by the public. The key link of this system is to quantitatively assess the contribution of individual carbon emission reduction, and build an effective incentive system based on this to cultivate individuals' low-carbon living habits based on the principle of "whoever reduces emissions, whoever benefits". This paper analyzes the development path of China's carbon preference system and the related suggestions, which can help stimulate the public to participate more actively in carbon emission reduction actions and help China's "dual-carbon" goal to be realized. This system will be continuously improved in practice and make greater contributions to China's green and low-carbon development.

MAS-DR: An ML-Based Aggregation and Segmentation Framework for Residential Consumption Users to Assist DR Programs

The increasing complexity of energy grids, driven by rising demand and unpredictable residential consumption, highlights the need for efficient demand response (DR) strategies and data-driven services. This paper proposes a machine learning-based framework for DR that clusters users based on their consumption patterns and categorizes individual usage into distinct profiles using K-means, Hierarchical Agglomerative Clustering, Spectral Clustering, and DBSCAN. Key features such as statistical, temporal, and behavioral characteristics are extracted, and the novel Household Daily Load (HDL) approach is used to identify residential consumption groups. The framework also includes context analysis to detect daily variations and peak usage periods for individual users. High-impact users, identified by anomalies such as frequent consumption spikes or grid instability risks using IsolationForest and kNN, are flagged. Additionally, a classification service integrates new users into the segmented portfolio. Experiments on real-world datasets demonstrate the framework’s effectiveness in helping energy managers design tailored DR programs.

Comparative modeling of household electricity consumption in France: Insights from path analysis and classical models

Electricity consumption in Europe has risen significantly in recent years, with households being the largest consumers of final electricity. Managing and reducing residential power consumption is critical for achieving efficient and sustainable energy management, conserving financial resources, and mitigating environmental effects. Many studies have used statistical models such as linear, multinomial, ridge, polynomial, and LASSO regression to examine and understand the determinants of residential energy consumption. However, these models are limited to capturing only direct effects among the determinants of household energy consumption. This study addresses these limitations by applying a path analysis model that captures the direct and indirect effects. Numerical and theoretical comparisons that demonstrate its advantages and efficiency are also given. The results show that Sub-metering components associated with specific uses, like cooking or water heating, have significant indirect impacts on global intensity through active power and that the voltage affects negatively the global power (active and reactive) due to the physical and behavioral mechanisms. Our findings provide an in-depth understanding of household electricity power consumption. This will improve forecasting and enable real-time energy management tools, extending to the design of precise energy efficiency policies to achieve SDG 7’s objectives.

Exploring heterogeneous responses of residential water consumption to lawn size: Insights from a novel parcel‐level dataset

Abstract This paper investigates the relationship between residential water consumption and lawn watering, considering precipitation, temperature, evapotranspiration, and lawn size. This paper uses a unique dataset comprising water‐billing information, parcel features, and weather data to explore how lawn dimensions drive water usage. The findings highlight the necessity of considering these factors in models of residential water demand and informing water conservation strategies. Two general findings emerge: Summer water consumption increases by 37% with a 10% expansion in lawn size, and monthly usage drops 52 gallons for each extra centimeter of precipitation in the preceding 30 days. Furthermore, the study examines the heterogeneity of responses among homeowners with varying household characteristics. The paper shows that newer homes have water consumption more strongly influenced by lawn size during summer, and higher‐value homes with larger lawns exhibit greater sensitivity to lawn size in the summer.

Ownership in times of water scarcity: are inhabitants supplied by private utilities consuming more water?

ABSTRACT The growing public focus on water resource issues highlights the effectiveness of demand-side management in reducing water consumption, a strategy adopted worldwide. In Brazil, where water scarcity remains a concern, the 2020 regulatory framework accelerated water utilities' privatization. This study examines how the expanded role of private companies in the water and sanitation sector influences residential water consumption behaviors. Using a three-level hierarchical linear model, we analyzed per capita water consumption data from 858 Brazilian municipalities between 2002 and 2019. Results show that, on average, residents in municipalities served by private companies consume less water than those served by public providers. While rising tariffs from privatization may partially explain these outcomes, the literature offers mixed support for this claim. Nonetheless, findings emphasize the importance of state-owned water providers, which serve most Brazilians, in revisiting their water demand management strategies. Key actions could include informational campaigns and economic incentives to promote voluntary water-saving behaviors.

Shaping a low-carbon future: Uncovering the spatial-temporal effect of population aging on carbon emissions in China.

With the accelerated development of the aging trend in Chinese society, the aging problem has become one of the key factors affecting sustainable economic and social development. Given the importance of controlling carbon emissions for achieving global climate goals and China's economic transformation, studying the spatial and temporal effects of population aging on carbon emissions and their pathways of action is of great significance for formulating low-carbon development strategies adapted to an aging society. This paper aims to explore the spatial-temporal effects of population aging on carbon emissions, identify the key pathways through which aging affects carbon emissions, and further explore the variability of these effects across different regions. The findings will provide theoretical support and empirical evidence for government departments to formulate policies to promote the coordinated development of a low-carbon society and an aging society. Based on the panel data of 30 provinces in China from 2004 to 2022, this paper systematically investigates the impact of population aging on carbon emission intensity from both spatial and temporal dimensions by using the spatial Durbin model and the mediating effect model. The direct effect of aging on carbon emission intensity, the spatial spillover effect, and the indirect effect through mediating variables such as residents' consumption, environmental regulation, and new urbanization are analyzed in depth. The study found that population aging in China has significant spatial and temporal effects on carbon emissions. From the spatial dimension, there is a significant spatial spillover effect of the effect of aging on carbon emissions, and aging reduces local carbon emissions but increases carbon emissions in adjacent regions. From the time dimension, the effect of aging on carbon emissions shows a stage characteristic, initially it will reduce carbon emissions, but with the deepening of aging, its effect may tend to weaken. In addition, this study identifies a number of key pathways through which aging affects carbon emissions, including reducing residential consumption, promoting new urbanization, and increasing the intensity of environmental regulations. Finally, this study explores the regional heterogeneity of the impact of aging on carbon emissions and its mechanism of action. This study is instructive: first, the complex impact of population aging on carbon emissions should be fully recognized to formulate a comprehensive low-carbon development strategy; second, attention should be paid to the spatial spillover effect of aging on carbon emissions to strengthen inter-regional cooperation and coordination; and lastly, differentiated low-carbon policies should be formulated to address the characteristics of aging in different regions and stages in order to promote the synergistic development of a low-carbon society and an aging society.

Hosting Capacity Enhancement Utilizing Small Pumped‐Hydro Storages in Rural Distribution Networks

Renewable energy sources (RESs) are growing exponentially due to need for sustainable energy. The hosting capacity (HC) is the amount of RESs that can be installed in a distribution network without exceeding its operational limitations, such as bus voltages and line flows. Energy storage systems (ESSs) have been utilized to enhance the HC in the literature. However, high investment cost of ESSs is the main obstacle to their widespread deployment. This highlights value of multipurpose energy storages (MPESs) that have multiple purposes besides the electrical aspect. Potential in the irrigation of the agriculture sector based on small pumped‐hydro storages (PHSs) has been employed in this paper to enhance the HC of rural distribution networks. For this purpose, a new practical model has been proposed for the PHS considering residential and agricultural water consumption management. Also, the HC of the distribution network for photovoltaic is investigated based on a mixed integer nonlinear programming (MINLP) model. Simulation results on the IEEE 33‐bus test system in GAMS and the comparison with the ESSs showed that the PHSs in addition to their main application increase the HC by 70 kW and reduce the cost and losses by 36 kWh and $47,562, respectively.