Electric Home Research Articles

Impact of newer climate data for technical analysis of residential building energy use in the United States

In this study, we describe an analysis indicating the relative importance of newer hourly weather files for simulation analysis of the energy use of residential buildings in North America.Results show that older TMY3 (ending 2005) data compared with newer TMY2021 (2007–2021) data are less appropriate for the best prediction of low-energy buildings. The balance of heating and cooling is significantly altered in the more recent TMY files with potential impacts on best performing energy efficiency measures. With 64 high profile North American locations evaluated, we found heating to be approximately 11% lower with the newer data, while cooling was increased by a similar amount percentage wise. However, the dominance of space heating in North America made decreases to heating considerably larger in an absolute sense so that overall space conditioning energy fell.Small changes in annual temperature can have large impacts. For all electric homes with air source heat pumps, we found that a change of 1 °C to outdoor temperature can produce about a 19 % change in space conditioning energy. The newer data also shows increased average solar electric output from simulated rooftop photovoltaic systems, likely due to more sophisticated solar irradiance data. Water heating was about 3 % lower given rising groundwater temperatures and inlet water temperatures to water heating systems. Results also showed that the newer data tended to increase savings from comprehensive building efficiency efforts in all electric buildings. This largely takes place because higher temperatures in winter allow air source heat pumps to meet heating needs more efficiently. Generally, the direction of these influences ease pathways to reach zero energy buildings in North America.

Assessing residential sustainable energy autonomous buildings for hot climate applications

This paper introduces an innovative solution for clean energy autonomous building (AB) sustainability, offering a 5Z concept—zero-carbon, zero-energy, zero grid connections, zero energy bills, and zero emission mobility. This paper focuses on fundamental research to design sustainable, energy-ABs striving for self-sufficiency in arid climates, using Kuwait as a case study. The study highlights the importance of stringent engineering AB modeling, renewable technology integration, and clean energy storage. The technical approaches include characterizing non-thermal and thermal demands, achieving net-zero energy generation, and custom sizing renewable energy and energy storage systems (ESS) for electric vehicle (EV) charging points. The research methodology involves local construction with yearly weather and energy profile data to simulate the actual system using ESP-r building model. The study's findings reveal the need for a large-scale energy system, energy demand reduction (EDR) inclusive of heating, cooling, appliances, and EV, with the corresponding changes in local energy production system size, battery capacity, and the number of photovoltaics (PVs). The results show varying EDR levels ranging from base case to 10% and to 50% leading to proportional changes in energy system size, size of battery from 3415 kWh to 1710 kWh and the number of PVs from 362 to 181, which means EDR not only optimizes space but also proves cost-effective. The significant implication of this study, not only bridging the knowledge gap and the lack of how-know, but also making a significant advancement and forward thinking in sustainable green electric home modernization and environmentally friendly transportation. This approach transforms energy management practices for more sustainable cities and societies, reducing emissions in both urban infrastructure and transportation.

Socioeconomic factors influencing residential occupancy trends during and post COVID pandemic

The residential building sector accounts for 22% of end-use energy consumption in the United States. Despite the strong influence of occupants’ behavior on energy consumption patterns in residential buildings, the impact of households’ socioeconomic background on occupancy is not well understood in the wake of the COVID-19 pandemic and its aftermath. This study aims to analyze the changes in occupancy patterns of residential buildings in the United States during and after the pandemic (2020–2022) using 14 socioeconomic variables. The American Time Use Survey (ATUS) data are used to define occupancy patterns; then correlation and regression analyses are applied to determine the most significant variables impacting the hours when members are at home. Results suggest employment status and household income level are the most significant variables predicting hours at home. Those under 25 years of age, low-income households, unemployed, and those identifying as Hispanic have most quickly returned to prepandemic (2018–2019) occupancy patterns. The results indicate that post pandemic (2022), occupancy patterns continue to change for those under 55, employed, and middle- and high-income groups, and thus must be monitored moving forward as they continue to evolve. These results are critical to help support ongoing electrification of homes and decarbonization of the electric grid.

The economics of home energy usage: Insights from urban economy

Recent regional investigations in the United States have revealed a thought-provoking perspective: household electricity consumption may act as an inferior commodity, displaying a negative correlation with wealth. This finding challenges the outcomes of various other econometric analyses and underscores the importance of scrutinizing power consumption patterns across different regional service areas. While it is commonly believed that home electricity usage decreases as income rises, this may not always hold true universally. This study focuses on power usage in Seattle, Washington households, a prominent urban economy in the Pacific Northwest. Employing dynamic error correction modeling techniques, the research demonstrates statistically significant fluctuations in domestic power usage attributed to variations in actual value, actual revenue, and cold weather. In the immediate future, energy for homes in this urban economy resembles any other commodity. However, investing in electricity for homes in Seattle may not be advisable in the long run. Home power usage in Seattle declines with each percentage point increase in actual per capita income over 1.2 %. This finding highlights the need for careful consideration and strategic planning in energy management policies for urban economies like Seattle.

A privacy-preserving distributed energy management framework based on vertical federated learning-based smart data cleaning for smart home electricity data

Nonintrusive load monitoring (NILM) is a part of home energy management systems ((H)EMSs), which can advance the systems to leverage and use gathered electricity data (load data) to achieve cost-effective load monitoring for efficient (residential) demand-side management (DSM). Load data monitored by existing (H)EMSs, whose load monitoring is based on intrusive load monitoring, can be used as a preliminary stage for NILM to be accelerated in energy management for the improvement of its practicality. However, they can be polluted. For example, power-consumption data gathered by appliance-level smart plugs may have an incorrect appliance label (a mislabeled appliance ID). Polluted data must be preprocessed through a data cleaning process before they are leveraged and used. Data-cleaning approaches that improve data quality should be developed considering a decentralized paradigm, because a centralized data-cleaning paradigm cannot be applied to future edge-based IoT applications. Additionally, preventing data leakage is paramount in data management for numerous field applications. This study develops a privacy-preserving distributed energy management framework based on vertical federated learning for smart data cleaning as a demonstrative application of smart home electricity data toward distributed NILM. The framework developed in this study with the advent of AI methodology can achieve smart data cleaning for further distributed NILM to be accelerated for its practical applications; its feasibility and effectiveness have been verified experimentally.

Who will ‘go electric’? American homeowners' perceptions of home energy sources and home electrification

In the context of the global climate crisis, the role the building sector plays in direct consumption of fossil fuels, especially natural gas, has come under increased scrutiny. Several major cities have initiated a phase out of natural gas hookups in homes and other buildings, requiring use of electric appliances. Additionally, with the passage of the 2022 Inflation Reduction Act, major incentives came available to help homeowners purchase and install electric appliances in their residences. Currently, over half of American homes use natural gas for heating or cooking, but research is needed to understand public opinion of household electrification. Here, we use 2021 survey data of American homeowners with electricity in the household (n = 4470) to analyze distribution of different energy sources currently used by homeowners, favorability toward gas versus electric appliances, and attitudes toward home electrification. We find that homeowners in the South, Midwest, and Intermountain West regions of the US are more likely than those in the New England area to already reside in electric-only homes, but we don't find the same geographic variation in support for home electrification. We also find that homeowners who are politically liberal, don't already use natural gas, have children at home, live in more urban areas, use rooftop solar, already use an induction stove or heat pump, and believe electricity is safer than natural gas are more supportive of electrification. These findings provide clues as to who is more and less likely to adopt electric household technologies.

Exploring the correlation between rising temperature and household electricity consumption: An empirical analysis in China

This study investigates the nexus between rising temperatures and household energy consumption using data from respondents' electricity bills in the "China Residential Energy Consumption Survey." Our analysis reveals a significant correlation, with an 8.9 % increase in yearly energy consumption observed when the average temperature exceeds 32 °C. Additionally, we explore potential shifts in power usage due to global warming by integrating baseline estimates with daily temperature forecasts from eight contemporary climate models. Our findings project alarming trends: without interventions to curb greenhouse gas emissions, home electricity consumption could surge by 9.59–30.09 % in the medium term and by 9.77–47.70 % in the long run. By shedding light on these critical connections, our research underscores the urgent need for policy actions to mitigate the adverse impacts of climate change on energy consumption patterns.

Introduction of Smallholder Horticulture Empowerment and Promotion (SHEP) approach as an innovative agricultural extension model

Agricultural extension in developing countries faces many challenges. The main challenge for public institutions, the implementers of agricultural extension services, is that they have weak implementation systems to support farmers. On the other hand, the main challenge faced by beneficiary farmers is low agricultural productivity and income. Against this backdrop, the Japan International Cooperation Agency (JICA) launched an initiative to promote “agriculture as a business” by strengthening the agricultural extension systems of developing country governments, beginning with a technical cooperation project implemented in Kenya from 2006 to 2009. The agricultural extension method developed in this project is the Smallholder Horticulture Empowerment and Promotion (SHEP) approach. The SHEP approach is supported by two theoretical pillars: promoting farming as a business (economics) and empowering and motivating people (psychology). A series of activities fulfilling these two pillars is the innovative originality of the SHEP approach. These activities are practiced according to four steps to promote market-oriented agriculture while motivating smallholders to improve their farming operations. In Kenya, horticultural income of target farmers increased through the practice of “grow to sell.” Following this result, the SHEP approach has spread across Africa through the Tokyo International Conference on African Development (TICAD), benefitting 256,546 small-scale farmers in 30 countries to date. In countries where the SHEP approach was implemented, in addition to the outcomes of increased horticultural productivity and income, many impacts were identified that contributed to improved farmers’ livelihoods, including repairing or building houses, purchasing mobile phones and vehicles, installing electricity in homes and funding children’s education. Since TICAD VII in 2019, JICA has been working with African governments, development partners, private companies and relevant organizations to promote the transition to profitable agriculture through the SHEP approach with the goal of achieving better lives for one million smallholders by 2030. This has led to the utilization of the SHEP approach in various forms, and its effectiveness and versatility has been recognized. However, inadequate government agricultural extension budgets and farmers’ high expectations for the provision of agricultural inputs raise the bar for intervention in the SHEP approach. The SHEP approach, however, is not a “panacea” that can solve all agricultural extension challenges in developing countries. Nevertheless, in order to continue agricultural and rural development programs/projects that promote farmers’ self-reliance, it is essential to continue to extract the outcomes and impacts of the SHEP approach at the field level and disseminate the effectiveness of the approach widely. Key words: agricultural extension, market-oriented, SHEP approach, smallholder, JICA, technical cooperation, Kenya, income

التنبؤ باستهلاك الكهرباء باستخدام منهج التحليل الطيفي في محافظة السليمانية

In the modern world, with the extensive use of electricity in homes and businesses, studying and forecasting electrical consumption is crucial for its management and distribution. This research aims to forecast the electricity consumption in Sulaimani province using spectral analysis on time series data. For this purpose, hourly data within the time range of July to September 2022 has been utilized, and a new model has been proposed, consisting of a sinusoidal explanatory variable and a nonlinear quadratic variable. In this model, the most significant periodic cycles present in the time series under study were identified by employing the periodogram and Fourier expansion method, capable of identifying hidden cycles in the data. Subsequently, each of these periodic components was added to the model based on the principle of parsimony. The proposed composite model includes a quadratic component and two harmonic components with 24 and 12-hour periods. This model demonstrates notable efficiency and accuracy as it outperforms the classical Autoregressive Moving Average (ARMA) model based on the MSE=0.0009 rate and MAE=23.819 when compared

Home electricity data generator (HEDGE): An open-access tool for the generation of electric vehicle, residential demand, and PV generation profiles

In this paper, we present the Home Electricity Data Generator (HEDGE), an open-access tool for the random generation of realistic residential energy data. HEDGE generates realistic daily profiles of residential PV generation, household electric loads, and electric vehicle consumption and at-home availability, based on real-life UK datasets. The lack of usable data is a major hurdle for research on residential distributed energy resources characterisation and coordination, especially when using data-driven methods such as machine learning-based forecasting and reinforcement learning-based control. We fill this gap with the open-access HEDGE tool which generates data sequences of energy data for several days in a way that is consistent for single homes, both in terms of profile magnitude and behavioural clusters.•From raw datasets, pre-processing steps are conducted, including filling in incomplete data sequences, and clustering profiles into behaviour clusters. Transitions between successive behaviour clusters and profiles magnitudes are characterised.•Generative adversarial networks (GANs) are then trained to generate realistic synthetic data representative of each behaviour groups consistent with real-life behavioural and physical patterns.•Using the characterisation of behaviour cluster and profile magnitude transitions, and the GAN-based profiles generator, a Markov chain mechanism can generate realistic energy data for successive days.

Design of Biogas Digester for Small Community of Salalah Countryside, Oman to Support the Lighting and Cooking Electrical Load

Bio-gas is a renewable energy source derived from the anaerobic digestion of organic materials such as agricultural, manure, and food waste. It emits methane and carbon dioxide, which reduce greenhouse gas emissions and promote circular economies by converting organic matter into valuable energy and nutrients. In this study, the biogas-based power generation possibilities are studied and reported based on the availability of biomass from animals like camels, sheep, goats, and cows. The data collection and analysis are carried out under different temperature conditions. The energy required is planned for eight people in each family. In the proposal work to design a digester, the location was chosen at the south-eastern part of the Arabian Peninsula (south of the Sultanate of Oman) for the place Madinat al Haqq. Afterwards, calculations are reported to estimate the size of the biogas digester and the output power generated from the digester. Here, the digester output is used for cooking and lighting for all members of the family. Further, the power output is viable based on the availability of biomass per day. The energy availability investigation is performed using the standard calorific value of the biomass per cubic meter. The proposed concept will allow the remote community to use a portal-sized biogas digester for their necessary electrical home load. Furthermore, the selection of the burner is reported to utilize the biogas efficiently.

Plugged into the Good Life: Living Electrically through the Ages in Urban Vietnam

With Vietnam's countrywide electrification rate approaching 100 percent, electricity has become ever more indispensable and essential to the quality of everyday life. The steady upsurge in household electricity consumption over the past years has been driven by a growing technification of the domestic sphere. Modern households are increasingly equipped with a variety of electric appliances that not only provide convenience and comfort but also serve as markers of social distinction. This essay explores the history of domestic electrification and people's aspirations to live electrically in urban Vietnam, from the colonial era until today. Inspired by recent advances in the anthropology of electricity, it investigates the meanings that people have attributed to electric home appliances through the ages and asks if, how, and why some of these have come to define what it means to live a good life. In the current era, however, living electrically has also come to reflect a global sense of “cruel optimism” in which people become increasingly attached to certain understandings of what constitutes a good life that are unsustainable in the long run, and detrimental to their well-being.

Nature-inspired solutions for energy sustainability using novel optimization methods.

This research centres on developing a Home Electricity Management (HEM) system, a pivotal component within the modern supply chain for home electrical power. The system optimizes the scheduling of intelligent home gadgets through advanced meta-heuristics, specifically the Social Spider Algorithm (SSA) and Strawberry Algorithm (SWA), to efficiently manage home energy consumption. Within the supply chain context, HEM acts as a crucial link in the distribution and utilization of electricity within households, akin to optimizing resource allocation and demand balancing within a supply chain for efficient operation and cost-effectiveness. Simulations and comparisons demonstrate that SWA excels in cost savings, while SSA is more effective in reducing peak-to-average power ratios. The proposed solution reduces costs for residences by up to 3.5 percent, highlighting the potential for significant cost savings and efficiency improvements within the home electricity supply chain. It also surpasses existing cost and Peak Average (PAR) ratio meta-heuristics, indicating superior performance within the overall energy supply and consumption framework. Moreover, implementing the HEM system contributes to reducing carbon emissions, aligning with sustainability goals in the energy supply chain. It promotes energy efficiency, integrates renewable sources, and facilitates demand response, mirroring the emphasis on sustainability in supply chain practices. Overall, this research offers a practical and sustainable approach to home energy management, bringing substantial cost savings and environmental benefits to the modern supply chain for residential electricity.

Mining research on correlation factors of residential electricity stability based on improved FP-growth algorithm

PurposeOn the one hand, this paper is to further understand the residents' differentiated power consumption behaviors and tap the residential family characteristics labels from the perspective of electricity stability. On the other hand, this paper is to address the problem of lack of causal relationship in the existing research on the association analysis of residential electricity consumption behavior and basic information data.Design/methodology/approachFirst, the density-based spatial clustering of applications with noise method is used to extract the typical daily load curve of residents. Second, the degree of electricity consumption stability is described from three perspectives: daily minimum load rate, daily load rate and daily load fluctuation rate, and is evaluated comprehensively using the entropy weight method. Finally, residential customer labels are constructed from sociological characteristics, residential characteristics and energy use attitudes, and the enhanced FP-growth algorithm is employed to investigate any potential links between each factor and the stability of electricity consumption.FindingsCompared with the original FP-growth algorithm, the improved algorithm can realize the excavation of rules containing specific attribute labels, which improves the excavation efficiency. In terms of factors influencing electricity stability, characteristics such as a large number of family members, being well employed, having children in the household and newer dwelling labels may all lead to poorer electricity stability, but residents' attitudes toward energy use and dwelling type are not significantly associated with electricity stability.Originality/valueThis paper aims to uncover household socioeconomic traits that influence the stability of home electricity use and to shed light on the intricate connections between them. Firstly, in this article, from the perspective of electricity stability, the characteristics of the power consumption of residents' users are refined. And the authors use the entropy weight method to comprehensively evaluate the stability of electricity usage. Secondly, the labels of residential users' household characteristics are screened and organized. Finally, the improved FP-growth algorithm is used to mine the residential household characteristic labels that are strongly associated with electricity consumption stability.HighlightsThe stability of electricity consumption is important to the stable operation of the grid.An improved FP-growth algorithm is employed to explore the influencing factors.The improved algorithm enables the mining of rules containing specific attribute labels.Residents' attitudes toward energy use are largely unrelated to the stability of electricity use.

Rancang Bangun Pengendali Jarak Jauh Panel Listrik Rumah Berbasis Mikrokontroler Arduino Uno R3 Menggunakan Smartphone Android dengan Komunikasi Bluetooth 3.0

The home electrical panel is a device that functions to divide electricity from the power source to the loads installed in the house. The operation of the Electrical Panel which is usually done directly by raising and lowering the Mini Circuit Breaker (MCB), then in making this Home Electrical Panel can be operated and controlled remotely without wires (remote). The principle of making this remote controller is the flow of electricity before the MCB is added, switching uses a relay that can be turned on or off by a controller based on the Arduino Uno R3 microcontroller and using an Android smartphone with Bluetooth communication. Programming or coding for Arduino microcontrollers and bluetooth modules using Arduino IDE software and making applications for Android smartphones using MIT App Inventor. Based on the results of the design and testing, by adding switching using a relay with a capacity of 220VAC-10A in each MCB-2A in the four areas of this electrical panel, each area can transmit and disconnect electrical power of 450VA controlled remotely wirelessly (remote) by operating from an Android smartphone with its Bluetooth communication feature with a range of up to 15 meters with obstacles and without obstacles it can still reach 20 meters. The results of this Electric Panel Design have 4 load areas that can be controlled remotely using an Android Smartphone to connect and disconnect electricity from the power source with a recommended maximum capacity of 2A 220VAC or equivalent to 450 VA per area.Keywords: Arduino Uno,Bluetooth, Microcontroller, Relays, smartphones.

Tracheostomy care quality improvement in low- and middle-income countries: A scoping review

Tracheostomy is a lifesaving, essential procedure performed for airway obstruction in the case of head and neck cancers, prolonged ventilator use, and for long-term pulmonary care. While successful quality improvement interventions in high-income countries such as through the Global Tracheostomy Collaborative significantly reduced length of hospital stay and decreased levels of anxiety among patients, limited literature exists regarding tracheostomy care and practices in low and middle-income countries (LMIC), where most of the world resides. Given limited literature, this scoping review aims to summarize published tracheostomy studies in LMICs and highlight areas in need of quality improvement and clinical research efforts. Based on the PRISMA guidelines, a scoping review of the literature was performed through MEDLINE/PubMed and Embase using terms related to tracheostomy, educational and quality improvement interventions, and LMICs. Publications from 2000–2022 in English were included. Eighteen publications representing 10 countries were included in the final analysis. Seven studies described baseline needs assessments, 3 development of training programs for caregivers, 6 trialed home-based or hospital-based interventions, and finally 2 articles discussed development of standardized protocols. Overall, studies highlighted the unique challenges to tracheostomy care in LMICs including language, literacy barriers, resource availability (running water and electricity in patient homes), and health system access (financial costs of travel and follow-up). There is currently limited published literature on tracheostomy quality improvement and care in LMICs. Opportunities to improve quality of care include increased efforts to measure complications and outcomes, implementing evidence-based interventions tailored to LMIC settings, and using an implementation science framework to study tracheostomy care in LMICs.

Impact of socio-demographic, psychological and emotional factors on household direct and indirect electricity saving behavior: A case study of Pakistan

The population of Pakistan increased by less than 2.26% while the demand for electricity increased by 4.95% annually. It implies that population expansion is the primary factor driving up home electricity usage. To effectively promote energy conservation and address the environmental and financial issues brought on by excessive electricity consumption, it is essential to understand the effect of socio-demographic, psychological and emotional elements affect household electricity-saving behavior. So, this study investigates how socio-demographic, psychological and emotional elements affect household electricity-saving behavior. The theoretical model is based on the theories of planned behavior and interpersonal behavior. Following that, a questionnaire-based survey is done to gather data from 415 homes in Pakistan's Punjab Province. Structural equation modeling (SEM) is used to evaluate the collected data and test the research's hypothesis. According to the findings, attitude, perceived behavioral control, personal moral nom, habit, and intention to save electricity are all significantly associated with electricity-saving behavior, whereas subjective norm, household, positive anticipated emotion, and electricity bill have no effect. The results we obtained highlight the significance of psychological and emotional elements as drivers of energy-saving behavior, implying that these non-cognitive and emotional factors require considerably more focus. By underlining the importance of emotional and psychological factors on household electricity-saving behavior, this research provides insightful data for policymakers, energy suppliers, and environmental organizations. By studying the combined effect of emotional and psychological elements on household electricity-saving behavior, stakeholders may build specialized ways to foster environmentally friendly energy practices and reach sustainable energy objectives in a range of socio-demographic scenarios.

SWOT analysis of a Floating Nuclear Power Plant for electricity generation in Brazil

The growing demand for electricity, increasing rising fossil fuel prices and global warming in Brazil, together with the lack of access to electricity on the part of the population in certain locations in the Northern Region, such as, for example, Rondônia, Pará and Amazonas, is currently seeking for solutions. The objective of the present study is to carry out a Strengths, Weaknesses, Opportunities and Threats (SWOT) analysis of the inclusion of a project of a floating nuclear power plants (FNPP) in Brazil with a view to solving the lack of electricity in homes and in industries located in remote regions of Brazil. Study results identified several socioeconomic and environmental impacts on this regions. The study concludes that in addition to contributing to the solution of the lack of energy in remote regions, the implementation of this proposal could represent in the future a source of inflow of financial resources in the country and of international collaboration. This SWOT study will encourage investors, researchers, engineers and decision makers to open the debate about the benefit of using a FNPP in Brazil, which in addition to providing economic energy rates for the population, would be a profitable business and an international collaboration.

An improved non-intrusive load identification using sample shifting and fuzzy rule-based technique

In this paper, a method to analyse load features utilising sample shifting technique (SST) for non-intrusive load identification (NILI) is presented and discussed. Fuzzy rules are used as the foundation for the identification logic. Voltage and current signals for electrical home appliances are acquired in order to develop their respective features. Two features like reactive power and total harmonic distortion for current (THDI), are created with the necessary computations of the samples using SST. A method based on fuzzy rules is created in order to identify different electrical equipment both for their individual as well as simultaneous running. Again, the performance of the proposed system is tested under the noisy environment while the accuracy of the system is found satisfactory. By utilising SST, the burden of computation is reduced in comparison to the other methods which are justified with the experimental results.

Enabling Remote Elderly Care: Design and Implementation of a Smart Energy Data System with Activity Recognition.

Seniors face many challenges as they age, such as dementia, cognitive and memory disorders, vision and hearing impairment, among others. Although most of them would like to stay in their own homes, as they feel comfortable and safe, in some cases, older people are taken to special institutions, such as nursing homes. In order to provide serious and quality care to elderly people at home, continuous remote monitoring is perceived as a solution to keep them connected to healthcare service providers. The new trend in medical health services, in general, is to move from 'hospital-centric' services to 'home-centric' services with the aim of reducing the costs of medical treatments and improving the recovery experience of patients, among other benefits for both patients and medical centers. Smart energy data captured from electrical home appliance sensors open a new opportunity for remote healthcare monitoring, linking the patient's health-state/health-condition with routine behaviors and activities over time. It is known that deviation from the normal routine can indicate abnormal conditions such as sleep disturbance, confusion, or memory problems. This work proposes the development and deployment of a smart energy data with activity recognition (SEDAR) system that uses machine learning (ML) techniques to identify appliance usage and behavior patterns oriented to older people living alone. The proposed system opens the door to a range of applications that go beyond healthcare, such as energy management strategies, load balancing techniques, and appliance-specific optimizations. This solution impacts on the massive adoption of telehealth in third-world economies where access to smart meters is still limited.