Residential Demand Research Articles

Social comparison of home appliance ownership and use and heterogeneous impact on electricity consumption: a case of apartment households in Korea

Efficient energy demand management in the residential sector must be designed based on a sufficient understanding of factors. This study focuses on apartments, a mainstream housing type in Korea. The combination of factor analysis and quantile regression analysis provides insight into the sociocultural meaning of home appliance ownership and use pattern and the differential impact on electricity consumption. The eight representative home appliances closely related to electricity consumption in the household sector are related to household composition, life cycle, economic ability, education, and occupation type. Additionally, the influence of home appliances on electricity consumption is not constant and is differentiated depending on the consumption level of each household. These attempts can provide customized information on specific housing type groups emerging in the future economic development and urban development process. In residential energy demand management, further segmentation and specification of the target and content of information provision will increase the effectiveness of policy achievement.

Estimating the direct rebound effect for residential electricity use in seventeen European countries: Short and long-run perspectives

The energy saved by improving the building efficiency of European households may not be as much as expected due to a rebound effect. Based on Eurostat and World Bank annual panel data covering the period 1996–2020, this article develops dynamic panel approaches, a generalized method of moments, and first-difference specifications, to investigate the direct rebound effect of residential electricity consumption in selected European countries. Instrumental variable techniques are used to tackle the reverse causality of the electricity price in a demand model. Estimates of the direct rebound effect in residential electricity use are 18% in the short-run and 43% in the long-run. Our findings reject the hypothesis of a backfire effect in residential electricity demand. They have important implications for policy makers, suggesting the need for smart policies that consider energy consumption behaviors and decision-making processes among various households.

A behaviour digital twin for residential demand response: Modelling intention and motivation to improve the prediction of the likelihood of reaction to behavioural triggers

Background: Residential demand response is a resource in the evolving energy infrastructure which thus far has not achieved its full potential. Amongst the reasons for this underutilisation is a lack of understanding, and therefore predictability, in relation to the uncertainty of the behaviour of human actors and its potential impact on energy demand side management. Optimal model predictive control of energy assets requires a digital twin to operate, however, most approaches so far are focused predominantly on technical indicators only and neglect the individuality of people and their behaviour in the operation. To fully integrate human led actions into such a system, the digital twin must therefore also provide social and psychological indicators to facilitate better predictability of reactions to demand response triggers. Methods: In the following a behaviour digital twin model will be presented based on the theory of planned behaviour and the self-determination theory, which provide well-established and validated tools to capture indicators of intention and motivation. The key contribution of this work is to operationalise and combine these models into a software tool, which continuously adapts its parameters to the evolving behaviour of users and provides up-to-date predictions. Results: The resulting model predicts the likelihood of each individual to react to appropriate demand response triggers, which can be used in model predictive control involving human actors to optimally select whom to target and when. Conclusions: The presented behaviour digital twin aims at bridging the gap between research in psychology to evaluate and assess drivers of behaviour and innovations in the space of model predictive control to optimally facilitate asset operation in residential settings.

Matchmaking in Off-Grid Energy System Planning: A Novel Approach for Integrating Residential Electricity Demands and Productive Use of Electricity

Off-grid electrification planning increasingly recognizes the importance of productive use of electricity (PUE) to promote community value creation and (financial) project sustainability. To ensure a sustainable and efficient integration in the community and energy system, PUE assets must be carefully evaluated to match both the community needs and the residential electricity demand patterns. We propose a novel methodology interlinking qualitative interviews, statistical analysis and energy system modeling to optimize decision making for PUE integration in off-grid energy systems in rural Madagascar by aligning relevant PUE effectively with anticipated residential electricity demand patterns based on socio-economic determinants of the community. We find that a possible contribution of the PUE to reducing the electricity costs depends significantly on three factors: (1) The residential electricity consumption patterns, which are influenced by the socio-economic composition of the community; (2) The degree of flexibility of (i) PUE assets and (ii) operational preferences of the PUE user; and (3) The capacity of community members to finance and operate PUE assets. Our study demonstrates that significant cost reductions for PUE-integrated off-grid energy systems can be achieved by applying our proposed methodology. When matching PUE and residential consumption patterns, the integration of PUE assets in residential community energy systems can reduce the financial risk for operators, provided the PUE enterprise operates reliably and sustainably. We highlight that the consideration of local value chains and co-creation approaches are essential to ensure the energy system is addressing the community’s needs, creates value for the community, enhances the project’s financial sustainability and is achieving the overall objectives of decentralized energy system planning.

How to allocate mitigation efforts between home insulation, fuel switch and fuel decarbonization? Insights from the French residential sector

Reducing greenhouse gas emissions in residential buildings relies on three channels that are rarely assessed together—insulating homes, switching to low-carbon heating systems and decarbonizing heating fuels. Their combination results from an interplay between top-down planning of the energy system and decentralized policies for the residential sector—insulation subsidies in particular. In this paper, we examine how the design of insulation subsidies influences the allocation of efforts between these three channels. To do so, we use an innovative framework coupling a highly detailed model of residential energy demand with a highly detailed model of the energy system, both focused on France. We find that the most cost-effective effort allocation to reach carbon neutrality implies 19% emission reductions from home insulation, 36% from fuel switch and 45% from fuel decarbonization. This however requires perfectly targeted subsidies. In three alternative, arguably more realistic subsidy scenarios, we find that total system cost is increased by 11%–16%. Our results highlight the key role played by subsidy specifications in determining the trade-off between insulation and fuel switch, e.g. insulation investments doubles, and heat pump adoption is 19% lower, when subsidies are restricted to the most comprehensive measures. Finally, alternative assumptions regarding the availability of renewable energy sources—biogas in particular—imply stronger energy efficiency efforts.

Performance Evaluation of Congestion Control Algorithm in TCP/IP Differentiated Services During COVID-19 Pandemic Due to Heavy Surge in Real-Time Traffic

Numerous nations implemented countrywide lockdowns because of the COVID-19 epidemic, which forced almost all peoples to remain at home. The imposition of confinement restrictions led to an increase in residential users' Internet traffic demands, particularly for distant work, entertainment, commerce, and education. This resulted in changes to the traffic patterns within the Internet core. In this research, we investigate the impact of the countrywide lockdowns on internet traffic based on dscp values by collecting data from class-A, class-B and Class-C ISP.(Three Class-A ISP, Three Class-B ISP and Three Class-C ISP) which leads to heavy congestion in the network and finally the performance valuation is carried out. The overall rise is exponential in nature, we notice that the traffic volume climbed by 15-30% almost in a quarter, which is a significant gain in such a short amount of time. Direct examination of the traffic sources reveals that, although hypergiants continue to account for a sizeable portion of traffic, there has been a greater increase in non-hypergiant traffic as well as in traffic from home-use applications like Web conferencing, VPNs, and gaming. Academic networks show significant overall declines whereas many networks, particularly those serving residential users, pharmaceutical companies, online interaction platform increases in traffic demands. However, we can see significant gains in these networks when we look at applications related to lecturing and remote working. The changes in the nature of use by customers show that there is a sharp difference in application pertaining to usage. Previous to this period, most usage is on YouTube, Facebook and google search. However, after this period most traffic diverted to online platforms such as zoom, google meet, Teams, and others as well as OTT platform comes into the picture. Over the past two decades, there has been a notable shift in user profiles. In the early 2010s, we saw most traffic was inclined on stored video and audio data in continuation with conventional traffic [23, 49, 66], which was followed by content delivery and streaming applications [7, 24, 35, 37, 52], and mobile applications [32, 67] in the 2010s. Even though user profile modifications are a moving target, they usually take years to complete. So, it was possible to stay current, for example, by measurements. The impact of this pandemic created chaotic conditions as most traffic migrated to real time surge data. Thus, the congestion management becomes more complicated as of previous challenges. The ISP forced to provide more bandwidth to real-time traffic which leads to overprovisioning. Thus, an efficient algorithm is required for managing such surge traffic which may especially meet real time traffic as required by most customers. This paper thus focuses on impact of after corona-19 pandemic effect, the shift of traffic by various users and applications the behavior of some existing algorithms in terms of congestion management and finally suggested a balanced method for managing congestion in such heavy surge traffic.

Optimizing microgrid efficiency: Coordinating commercial and residential demand patterns with shared battery energy storage

The optimization of energy systems within a multi-microgrid framework, enriched by shared Battery Energy Storage Systems (BESS), has emerged as a compelling avenue for enhancing the efficiency of distributed energy networks. In response to the increasing integration of BESS in modern energy systems, this study investigates the implications of incorporating BESS within connected residential-commercial Microgrids (MGs). Unlike previous studies that primarily focused on cost and reliability, this research fills a significant gap in the literature by investigating the optimization of load demands patterns. Specifically, we explore the impact of shared BESS on load demand patterns in commercial-residential MGs. The research introduces two innovative critical load metrics, peak-to-average ratio (PAR) and demand profile smoothness (DPS), to assess the influence of BESS on demand profiles. In addition, the study explores the integration of a Dynamic Thermal Rating (DTR) system, compared to traditional fixed thermal rating systems, to further optimize the performance and efficiency of connected residential-commercial MGs enriched by shared BESS. Three distinct case studies, each comprising a commercial MG (shopping mall, hotel, and office building) paired with a residential MG, were considered. Utilizing a Firefly Algorithm (FA) for optimization, the study determines the optimized BESS capacity for minimum total cost. The results highlight that the implementation of shared BESS, especially in collaboration between commercial and residential MGs, significantly reduces imported energy from the main grid, enhancing MG flexibility and resilience. While the economic benefits of shared BESS may not be substantial (up to 3.25 % cost reduction), the study underscores its contribution to more balanced and smoother load demand curves, with improvements in PAR (up to 15.63 %) and DPS (up to26.05 %). Moreover, considering DTR for transmission lines, instead of fixed thermal rating, improves the PAR and DPS up to 28.52 % and 41.06 % respectively. The findings of this study highlight the importance of considering load demand patterns in the design and operation of MGs and underscore the multifaceted benefits of shared BESS beyond economic considerations.

Time-series data clustering with load-shape preservation for identifying residential energy consumption behaviors

Categorizing residential energy demand patterns is a principal task for demand-side management (DSM) and energy-saving strategies. While deep learning (DL)-based clustering offers a promising alternative to conventional machine learning (ML), DL’s advantages and disadvantages over ML still remain unclear in identifying energy demand patterns. Moreover, prevalent DL-based clustering can suffer from catastrophic feature distortion when capturing load-shape information from energy-load data, leading to erroneous pattern identification. To address these issues, we propose integrating a load-shape preservation mechanism into representative DL-based clustering and investigate its effectiveness in categorizing energy demand patterns, compared to existing ML and DL. We experiment and compare the three clustering approaches using one-year residential energy-load data. Results show that the proposed DL, equipped with load-shape preservation, outperformed ML quantitatively and closely aligns with the baseline DL’s performance. This is particularly significant considering that the baseline DL prioritizes quantitative enhancements, sometimes compromising load-shape precision. Furthermore, the proposed DL discovered more diverse energy demand patterns than the baseline ML and DL, while producing more human-agreeable results. This finding underscores the pivotal role of load-shape preservation in enhancing data clustering and demand pattern recognition in the real-world. These benefits will facilitate personalized DSM interventions and foster residents’ energy-saving behaviors.

Quantifying residential energy flexibility potential for demand response programs using observational data from grid outages: Evidence from Pakistan

Over the past few years, incentive- or price-based demand response programs targeting residential energy demand have become increasingly popular in high-income countries. Such programs aim to shift energy demand from times when there is limited supply to times of abundance, in order to ensure stable grid operation. However, despite their cost-effectiveness, these schemes have gained little traction in low-income countries, where the amount of residential flexibility cannot be accurately quantified due to a lack of existing demand response studies, and grid operators continue to rely on the blunt instrument of grid outages to manage demand. In this paper, we propose a framework to quantify the flexibility using existing grid outages. Applied to high-resolution sub-metered data from 42 households in Lahore (Pakistan), we show that the proposed method is able to quantify available flexibility at the residential or feeder-level in an automated manner. The proposed method can also be used to evaluate the efficacy of grid outages as a strategy to shift or curtail demand, which can in turn be used to design better demand response schemes. Our results provide evidence that load-shedding causes significant rebound peaks due to shifted demand, while overall energy demand is actually increased rather than curtailed due to the mitigation strategies adopted by households. The paper concludes with the most important policy implications, especially with the rapid proliferation of distributed solar PV systems.

Exploring gender differences in residential water demand

Residential water demand has been extensively studied, with the impact of various household characteristics on consumption well-documented. However, the specific effect of gender on household consumption remains insufficiently identified due to the predominant focus on mixed-gender households in previous research. In this paper, we aim to address this gap by examining gender differences in water consumption specifically within single-gender households. To accomplish this, we analyze data from 275 households equipped with individual meters in the city of Gijón, Spain, between 2017 and 2021. Our approach involves two main steps: first, the estimation of a Stone-Geary demand function for water consumption for both women and men single-gender households, and second, employ the Oaxaca-Blinder decomposition to examine gender differences based on the previous estimations. Our findings reveal that women's households consume significantly more water compared to men's households. Additionally, we observe that the demand for water is more inelastic among women, and their level of conditional use threshold is higher than that of men. Importantly, we find that these differences can be primarily attributed to distinct factors such as family composition, housing characteristics, and bill information between genders.

Estimating Residential Water Demand Under Systematic Shifts Between Uniform Price (UP) and Increasing Block Tariffs (IBT)

AbstractWe evaluate whether changing from a uniform price (UP) to an increasing block tariff (IBT) changes people's behavior. We exploit a unique setting in which the price scheme moves back and forth yearly from UP to IBT. We discuss the effectiveness of IBT in reducing summer consumption. This issue is relevant to many countries and policymakers interested in designing tariff structures. There is no evidence of how the same consumer may react to systematically switching from one tariff structure to another yearly. We estimate the residential water demand and its price elasticity using a generalized least squared random effect model for the UP and the discrete/continuous choice model for the IBT. In addition, we split the sample between low and high‐consumption groups. For the low consumption group unaffected by the tariff change, the elasticity in the nonsummer months is higher (more elastic) than in the summer. Consumers in this group reduce their elasticity from nonsummer to summer months (−0.299 vs. −0.071, respectively) and increase their consumption by 13%. The high consumption group increased its summer consumption, but only by 8.7%, and contrary to the first group, its elasticity increased significantly (from −0.299 to −0.568). The high‐consumption group is indeed affected by the change in tariff. From a policy perspective, this implies that the IBT structure is relevant. However, if the policy seeks to promote conservation, it needs to be adjusted to a lower decile of the water consumption distribution to affect a more significant portion of the population.

The Role of Speculative Demand in Housing Price Changes in Turkey

Housing accounts for the largest portion of households' total assets in Turkey. It is an economic sector that requires careful analysis, especially considering the government's housing sector support policies and the significant price increases in recent years. This study utilizes the theoretical model developed within the Efficient Markets Hypothesis framework and the SVAR methodology to estimate house price changes. These changes are then decomposed into structural shocks, including supply, residential demand, and speculative demand shocks. The empirical results highlight the significant role of speculative demand in driving house price changes, with house price expectations being the main factor influencing speculative demand shocks. The behavior of the banking sector and interest rates also play a significant role in this process. Consequently, policies to stabilize house prices should consider these factors and not solely focus on credit regulations when consumer expectations are the driving force behind house price changes.

Experimental and simulation investigation of a Savonius vertical axis wind turbine for residential applications: a case study in Indonesia

The aim of this study was to investigate several geometric designs to improve the performance of a Savonius vertical axis wind turbine (SVAWT) at low wind speeds. The SVAWT models were numerically tested using ANSYS prediction software to determine the best performance. Furthermore, a SVAWT prototype was experimentally evaluated in subsonic wind tunnels at wind speeds of 1–5 m/s to determine the electrical power produced. The experimental results were used as input parameters for HOMER tools to optimise renewable energy systems and establish the prototype’s techno-economic environmental viability for satisfying the residential demand load. The simulation results showed that the maximum power coefficient obtained was 0.25 for a SVAWT model with a twist angle of 45° at a wind speed of 5 m/s. The experimental results showed that the modified SVAWT produced 494.9 W of electrical power at a wind speed of 5 m/s. The results confirmed that the cost of electrical energy following an initial investment in the optimised renewable energy system configuration (a grid-connected 1-kW SVAWT with a – kW electrical converter and two battery strings) was 0.0963/kWh and 4,425. This setup achieves a 65.8% renewable fraction, cutting CO2 emissions by over 53.71%, fit for residential use.

Inform me when it matters: Cost salience, energy consumption, and efficiency investments

Using a large-scale natural experiment in staggered billing dates for energy use in Germany and a unique billing dataset for multi-apartment buildings, this paper shows that the month of billing is a significant determinant of heat energy consumption. A large set of residential buildings demand significantly more heat energy annually, when the bill is issued during off-winter months. The paper finds evidence for salience cycles of heating bills that last up to 4 months, likely because consumer attention to heat energy costs is short-lived and absent during months when heating is off. Importantly, this phenomena is pervasive enough to be detectable even in aggregated building-level consumption data. Results suggest that the mere knowledge of costs is not sufficient and that the response to billing information also hinges on its time-varying salience. These findings underscore the importance of understanding the dynamic and heterogeneous nature of cost salience in the design of effective billing for energy conservation.

Impact of bi-directional electric vehicle and demand response on residential distributed PV capacity planning based on TOU pricing

The widespread deployment of residential distributed photovoltaic (RDPV) remains complex and challenging due to photovoltaic output intermittency, fluctuating electricity demand, and rising electric vehicle (EV) adoption. Simultaneously, the energy storage capabilities of EVs and residential demand response (DR) offer solutions for optimizing RDPV applications. This study proposes an integrated RDPV capacity planning model by encompassing EV charging, vehicle-to-home, and flexible load DR. Five scenarios are established to reveal the impact of various factors on the optimal photovoltaic installation capacity, electricity cost, self-consumption and self-sufficiency rate. A case study of three typical residential electricity demand patterns indicates that DR and vehicle-to-home significantly reduce the optimal photovoltaic installation capacity and total electricity cost. When the feed-in tariff during photovoltaic generation periods is higher than the off-peak pricing, DR results in a reduction in photovoltaic self-sufficiency rate and an increase in photovoltaic self-consumption rate. EV charging and vehicle-to-home have minimal impact on photovoltaic self-consumption rate, while EV charging significantly decreases self-sufficiency rate and vehicle-to-home exacerbates this effect.

Validating ‘GIS-UBEM’—A Residential Open Data-Driven Urban Building Energy Model

The study of energy consumption in buildings, particularly residential ones, brings with it significant socio-economic and environmental implications, as it accounts for approximately 40% of CO2 emissions, 18% in the case of residential buildings, in Europe. On a number of levels, energy consumption serves as a key parameter in urban sustainability indicators and energy plans. Access to data on energy consumption is crucial for energy planning, management, knowledge generation, and awareness. Urban Building Energy Models (UBEMs), which are emerging tools for simulating energy consumption at neighborhood scale, allow for more efficient intervention and energy rehabilitation planning. However, UBEM validation requires reliable reference data, which are often challenging to obtain at urban scale due to privacy concerns and data accessibility issues. Recent advances, such as automation and open data utilization, are proving promising in addressing these challenges. This study aims to provide a standardized UBEM validation process by presenting a case study that was carried out utilizing open data to develop bottom-up engineering models of residential energy demand at urban scale, with a resolution level of individual buildings, and a subsequent adjustment and validation using reference tools. This study confirms that the validated GIS-UBEM model heating and cooling demands and consumption fall within the confidence bands of ±15% and ±12.5%, i.e., the confidence bands required for the approval of official alternative simulation methods for energy certification. This paves the way for its application in urban-scale studies and practices with a well-established margin of confidence, covering a wide range of building typologies, construction models, and climates comparable to those considered in the validation process. The primary application of this model is to determine the starting point and subsequent evaluation of improvement scenarios at a district scale, examining issues such as massive energy rehabilitation interventions, energy planning, demand analysis, vulnerability studies, etc.

Social Recommendation Framework: A Case Study of Chinese Long-Stayers in Chiang Mai

Chiang Mai (CNX), a popular city in northern Thailand, has attracted an increasing number of Chinese visitors to stay for a long term for diverse purposes, which facilitates local economic and cultural development. As social networks (SNs) are widely used to disseminate information and accelerate problem-solving, social recommendations (SRs) can be generated correspondingly to address the diverse and dynamic long-term residential demands of Chinese users in a multicultural context. This research aims to develop an SN-based recommendation framework for Chinese long-stayers in CNX to address the social recommendation problems for target long-stay users in a cross-cultural context. This paper employed a mixed-method research design based on the knowledge management processes to acquire, store, share and apply knowledge needed for SN analysis. The results showed that the proposed framework effectively provides filtering and efficient SRs that enable Chinese users to make decisions and formulate strategies during their long-term residence in CNX. The preliminary work also illustrates the positive impact of individual demographic and SN characteristics on the performance of SRs that researchers and practitioners need to develop innovative business and management strategies regarding Chinese long-stayers in a cross-cultural context. Further studies are required to identify additional factors to enhance the effectiveness and utility of SRs for Chinese long-term residents in a cross-cultural environment.

Transforming the Water-Energy Nexus in Gaza: A Systems Approach.

The acute water and electricity shortages in Gaza necessitate comprehensive solutions that recognize the interconnected nature of these vital resources. This article presents pragmatic solutions to align supply with fundamental needs in both domains, offering viable pathways for achieving strategic water-energy security in Gaza. Baseline data reveals a deficit in the current water supply, falling below the international minimum of 100 L per capita per day, while the reported 137-189 MW per day electricity supply significantly lags behind the estimated 390 MW per day peak demand. To meet projected 2024 residential, commercial, and industrial demands, this study proposes actionable measures including expanding wastewater treatment to enable over 150 MCM per year tertiary effluents for agricultural reuse and adopting energy-efficient forward osmosis-reverse osmosis and osmotically assisted reverse osmosis desalination methods to increase potable water supply to 150 MCM per year. Electricity supply strategies include scaling renewable capacity towards 110 MW per day, exploring regional cooperation to unlock over 360 MW of power per day, and potentially recovering up to 60 MW per day through system efficiencies. These recommendations aim to prevent exacerbated scarcity and alleviate hardships in Gaza.

Predicting Ghana’s Daily Natural Gas Consumption Using Time Series Models

In recent years, natural gas utilisation has seen a considerable increase because, it presents an alternative energy source that is reliable, economical and environmentally friendly for consumers. In Ghana, natural gas consumption has over the years increased due to mainly the rise in industrial and residential demands. Accurate prediction of natural gas consumption will provide stakeholders with vital information needed for planning and making informed policy decisions. This paper explores the Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA) to predict Ghana's daily natural gas consumption. The data employed for the study is daily natural gas consumption in Ghana from 2020 to 2022. The results show that both ARIMA and SARIMA models can predict the consumption of natural gas in Ghana with a good degree of accuracy. The SARIMA model slightly outperforms the ARIMA model, with a Root Mean Square Error (RMSE) of 22.25 and a Mean Absolute Percentage Error (MAPE) of 6.96%, compared to an RMSE of 23.27 and a MAPE of 7.29% for the ARIMA model. The model forecast suggests a steady natural gas consumption in Ghana but with some intermittent fluctuations.

THE IMPACT OF INTERGENERATIONAL PARENTING ON AC COOLING DEMAND: EVIDENCE FROM SOUTHERN CHINA

Given ever-increasing temperatures, household air conditioner (AC) usage is increasing rapidly, posing significant sustainability challenges. Identifying the drivers of residential demand is critical, especially in developing countries such as China. In contrast to assumptions made by the existing literature, intergenerational parenting and multigenerational households are widespread in China. To address this oversight, this study is the first to use data from an appliance-level, 15-min AC operation database from southern China to quantify the impact of intergenerational parenting families on cooling-related electricity consumption and examine its mechanisms. First, intergenerational parenting significantly positively impacts such consumption. Compared to working-age households, Intergenerational households consume 49% more electricity for cooling, and families with children consume 28% more. In contrast, families with older adults but no children use 6% less electricity for cooling than working-age families. Second, intergenerational families use the AC for 0.5 additional hours per day, set the temperature 0.9°C lower, and keep the AC idle longer than working-age households. Third, intergenerational parenting’s impact shows an M-shaped pattern throughout the day (peaking at 9:00 and 17:00). Fourth, working-age adults significantly moderate the impact of intergenerational parenting on electricity demand.