Household Appliances Research Articles

A method of distributed series compensation for low voltage management in power distribution network

In some areas with sparse populations and dispersed power loads, the construction investment of distribution network lines cannot keep up, and the phenomenon of low voltage at the end of the power distribution network is particularly serious, even causing some household appliances to be unable to start. This article aims to provide some solutions to alleviate the low voltage of distribution network lines in certain regions. It introduces a method based on distributed series compensation for long lines with low voltage of the line terminal. In the same distribution network line, it is installed two or more sets of series compensators to alleviate the low voltage problem at the end of the long line of the distribution network based on a decentralized arrangement. The technical scheme is based on a 10 kV feeder for simulation to verify the correctness and effectiveness of the proposed method.

Unknown appliances detection for non-intrusive load monitoring based on vision transformer with an additional detection head

Non-intrusive load monitoring (NILM) offers precise insights into equipment-level energy consumption by analyzing current and voltage data from residential smart meters, thus emerging as a potential strategy for demand-side management in power systems. However, a prevalent limitation in current NILM techniques is the presupposition of a known inventory of household appliances, an assumption that often becomes impractical due to the regular introduction of new appliances by consumers. To address this challenge, our approach integrates a vision transformer network with an additional detection head (ViTD), utilizing V-I trajectory images. Initially, the ViT model is trained to classify known appliances. Subsequently, an additional detection head is incorporated to manipulate the embedded features, encouraging the formation of distinct, compact class centers for the known appliance categories. During testing, samples are identified as either known or unknown appliances based on their proximity to these class centers. We utilize two public datasets, PLAID and WHITED, to demonstrate the effectiveness and superiority of our proposed method.

IoT Smart Electricity Energy Meter

Abstract: The escalating demand for electric energy, coupled with concerns over sustainability and resource depletion, underscores the need for innovative solutions to monitor and manage electricity usage. This project proposes an Internet of Things (IoT) based household appliance monitoring system to address these challenges. By integrating hardware, software, and cloud technologies, the system provides real-time insights into energy consumption at both the household and device levels. Additionally, it offers transparency and accountability by displaying energy usage and costs, empowering users to make informed decisions to optimize their electricity expenses. Furthermore, the system extends its application to electric vehicle (EV) charging, enhancing efficiency and convenience in this growing domain. Overall, this IoT-based energy monitoring system represents a crucial advancement towards a more sustainable and efficient energy future.

Exploring the Influence of Green Product Attributes on Consumer Perceived Value and Purchase Intention: Taking Green Household Appliances as an Example

Purpose - This study takes green household appliances as an example to investigate how green product attributes impact consumers’ perceived value and purchase intention. It further explores the moderating role of consumer innovativeness and mediating role of perceived value. Design/Methodology/Approach - The research participants were 259 Chinese consumers, whose relevant data were collected through a questionnaire survey. The data analysis was performed with structural equation modeling using SmartPLS. Findings - The results indicate that green products’ core (functional and value-for-money) and peripheral (emotional and social) green attributes both positively influence consumer perceived value, which, in turn, positively affects their purchase intention. Perceived value plays a positive mediating role between green product attributes and consumer purchase intention. However, the moderating effect of consumer innovativeness is not significant. Research Implications - First, this study explores the impact of green product attributes on consumer purchase intention from multiple dimensions, enriching empirical research on green product attributes. This provides a theoretical foundation for enterprises to strategically implement green product marketing strategies. Furthermore, based on the research findings, we recommend that relevant enterprises emphasize green product and technology research and development, guiding consumers toward environmentally friendly purchasing behavior with targeted approaches. This, in turn, will lead to ongoing development of the green market and green consumption.

Exploring Brand Loyalty in Online Shopping: The Context of Vietnam's Household Appliances Market

In the dynamic e-commerce landscape, where online shopping has become a fundamental aspect of consumer behavior, a profound comprehension of the factors influencing brand loyalty is imperative for businesses striving to excel in the digital marketplace.This study aims to investigate the factors influencing brand loyalty of consumers in Viet Nam, with a specific focus on household appliance brands. Additionally, it seeks to elucidate the mediating role of customer satisfaction and brand trust in influencing brand loyalty. The study provides valuable information for developing effective brand strategies, helping strengthen customer loyalty, and increasing market share for home appliance brands. The research will employ a qualitative and quantitative approach, gather primary data through a structured questionnaire survey. The data analysis will be conducted using Smart PLS 4 and SPSS 26. The results of the study indicate that there are several factors influencing brand loyalty, including brand image, brand indentification, brand awareness, brand trust, and customer satisfaction. This underscores the importance of applying the research findings to optimize brand management and marketing strategies. Paper type Research paper

Household durable goods and child health in China

Abstract This article uses micro-level data from the China Health and Nutrition Survey to determine how ownership of time-saving household appliances impacts children’s health. Ownership of these appliances is found to decrease time allocated to household work by 56 min and increase the incidence of being overweight by 9.7 percentage points for children aged 5–18 years. When the sample is partitioned on the basis of gender, these outcomes are more pronounced among males. I instrument household ownership of time-saving appliances by average ownership rate among households with no children living in the same community as ownership of household durable goods is endogenous. With current concerns about rising rates of obesity and overweight in China, understanding some of the causes of these negative health outcomes is a crucial step in fighting childhood obesity.

The Effect of Company Size and Working Capital on Net Income (An Empirical Study of Manufacturing Sector Companies in the Consumer Goods Industry, Home Appliances Sub-Sector Ladder Registered on the IDX from 2015 to 2020)

This study examines how working capital and firm size affect net profit (Empirical Study of Manufacturing Companies in the Consumer Goods Industry Sector, Household Appliances Sub Sector Listed on the IDX for the 2015 – 2020 period). Multiple linear regression analysis was used to determine the effect of two independent variables on one dependent variable. The population in this study is financial reports published by Manufacturing Companies listed on the IDX in the Consumer Goods Industry Sector, Household Appliances Sub-Sector. The sample was taken for six years, from 2015 to 2020, using the Financial Position Report and Profit and Loss Reports to obtain data, Company Size, Working Capital, and Net Income. According to the study's findings, firm size and working capital both have positive and substantial effects on net profit, with the latter having an influence on net profit that is both positive and significant. Other factors that influence Net Profit but are not analysed include the 83.3% outcome of the Coefficient of Determination and the remaining 16.7%.

Factors impacting human exposure to legacy and emerging contaminants in residential dust in Beijing, China: Characteristics of indoor microenvironment

Exposure to indoor dust is of concern since dust may be contaminated by various toxic chemicals and people spend considerable time indoors. Factors impacting human exposure risks to contaminants in indoor dust may differ from those affecting the loadings of contaminants, but the dominant factors have not yet been well clarified. In this study, the occurrence, human exposure, and related influencing factors of several classes of legacy and emerging contaminants in residential dust across Beijing were investigated, including per- and polyfluoroalkyl substances (PFASs) and three types of flame retardants (FRs), i.e., organophosphate esters (OPEs), polybrominated diphenyl ethers (PBDEs), and novel halogenated FRs (NHFRs). OPEs (median: 3847 ng/g) were the most abundant group, followed by PBDEs (1046 ng/g) and NHFRs (520 ng/g). PFASs (14.3 ng/g) were one to two orders of magnitude lower than FRs. The estimated daily intakes of these contaminants were relatively higher for toddlers than other age groups, with oral ingestion being the main exposure pathway compared with dermal contact. Higher human exposure risks were found in new buildings or newly finished homes due to the elevated intake of emerging contaminants (such as OPEs). Furthermore, higher risks were also found in homes with wooden floors, which were mainly associated with higher levels of PFASs, chloroalkyl and alkyl OPEs, compared with tile floors. Citizens in the urban area also showed higher exposure risks than those in the suburban area. The quantity of household appliances and finishing styles (simple or luxurious) showed an insignificant impact on overall human exposure risks despite their significant effect on the levels of some of the dust contaminants. Results in this study are of importance in understanding human exposure to the co-existence of multiple contaminants in indoor dust.

Fostering Design for Sustainability through the Adoption of Computer-Aided Engineering Tools in the Development of Energy-Related Products

The main challenge to face in the development of energy-related products is represented by the adoption of effective design for sustainability strategies that encompasses the adoption of engineering design tools, knowledge collection, and reuse/sharing in technical departments. This present paper proposes an engineering design for sustainability methodology that assists engineers in developing energy-related products in compliance with ecodesign standards. The methodology uses virtual prototyping tools to assess energy consumption in compliance with energy labeling directives and analyze different use scenarios. The results obtained by numerical simulations (e.g., Finite Element Method—FEM, Computational Fluid Dynamics—CFD) are used to create specific design eco-knowledge in the field of energy-related products. Numerical results are linked with design configurations to understand the benefits introduced by engineering design choices. This knowledge is stored in a structured database with the aim of being reused when a new product is developed or improved/upgraded. The case study of an induction hob, belonging to the household appliance product family, is investigated to understand the potential and drawbacks of the presented approach in a real application. The results show that potential energy and environmental performance benefits are achieved (e.g., reduction of energy losses, achievement of A+ energy class, and overall life cycle environmental impact reduction). Additionally, a new set of ecodesign guidelines are defined for this product family and employed in developing new compliant products belonging to the same family.

DNA sequences for robust encryption: a strategy for IoT security enhancement

As the Internet of Things (IoT) permeates our lives, connecting everything from household appliances to complex industrial systems, the imperative to secure these devices intensifies. Cryptography, as a cornerstone of digital security, plays a crucial role in safeguarding transmission channels from intrusions and misuse. Cryptography secures communications and data within IoT networks by ensuring three key functions: confidentiality, integrity, and authentication. DNA-based cryptography emerges as a promising innovation in the field of cybersecurity, particularly for the Internet of Things (IoT), where data and communication security is an escalating concern. Utilizing the unique properties of DNA, such as its massive storage capacity and biomolecular complexity, this approach introduces a novel dimension of security. This study introduces a balanced approach within DNA cryptography to enhance message security in Internet of Things (IoT) settings. It outlines a method for creating secure symmetric keys using DNA sequences, typically derived from human chromosomes, and then applies biological techniques like transcription and a biological Xor operation. This step is succeeded by a translation phase that utilizes an index table created from an initial key, making the process more complex.

Intelligent Energy Management: Leveraging an Effective Machine Learning for Predictive Appliance Energy Optimization in Smart Homes

In the context of escalating energy demands and the proliferation of smart home technologies, this study introduces a novel approach to energy management using the Random Forest machine learning model. Our research focuses on optimizing household appliance energy use, harmonizing efficiency with user comfort. By analyzing data on appliance usage patterns, environmental conditions, and user preferences, the Random Forest model predicts future energy needs, enabling the intelligent scheduling of appliances to reduce unnecessary consumption. The model’s strength lies in its capacity to unravel complex, non-linear relationships in high-dimensional data typical of household energy usage scenarios. Initial results demonstrate a notable decrease in energy consumption, affirming the model’s efficacy in enhancing energy efficiency without diminishing user convenience. This research not only highlights the potential of machine learning in energy management but also sets a foundation for future exploration into adaptive, real-time energy optimization strategies in smart homes. In this research, we compared our model with other machine learning models, and our model got a good accuracy of 95.71%, while for time series data, we got 99.71%.

Impact of Internet of Things on Home Appliances

The way household appliances operate and communicate with consumers has completely changed as a result of the Internet of Things (IoT). This abstract explores how the Internet of Things affects household appliances, emphasizing how it affects user experience, convenience, and energy efficiency. With the use of smartphones and other smart devices and IoT connectivity, users can effortlessly integrate and control their appliances, giving them the ability to monitor and operate them remotely from any location. Tasks like receiving notifications, changing settings, and even starting appliance functions when away from home are made possible by this convenience. Furthermore, by using automation and data-driven insights, IoT makes it possible for home appliances to operate more energy efficiently. With sensors and connectivity, smart appliances can adjust their performance according to usage patterns and environmental factors in real time, saving energy and money on utility bills. IoT-enabled appliances can also take part in demand response initiatives, which helps with overall energy saving efforts. IoT-enabled appliances greatly improve user experience with their customizable features and user-friendly interfaces. The performance and lifespan of appliances are increased by machine learning algorithms that examine user behavior and preferences to make personalized suggestions, optimize settings, and foresee maintenance requirements. Keywords: Communication, Smart devices, Connectivity, Remotely, Sensors, Automation, Customizable features, User- friendly, Energy efficiently

Household energy consumption of herders on the Qinghai–Tibetan Plateau: Profiles of natural and socio-economic factors

In remote areas, people experiencing persistent poverty still face challenges related to energy shortages and air pollution. Due to the escalating global climate change crisis, herding communities in the Qinghai–Tibetan Plateau remain vulnerable to significant threats related to energy security and accessibility. These communities are vulnerable because they occupy underdeveloped regions and depend heavily on traditional energy sources. To explore this issue, we conducted a comprehensive questionnaire-based survey involving 473 herders across 13 counties on the Qinghai–Tibetan Plateau. The findings revealed that per capita energy consumption in herder households averaged 2021.793 kgce/year. Noncommercial energy sources constituted a substantial 61.66%, with the predominant one being cow dung, and refrigerators are the highest energy consumption household appliances. By applying geographically weighted regression (GWR), we examined seven key natural and socio-economic factors influencing solid energy use. These factors included annual per capita household income, the highest level of education, indoor and outdoor oxygen concentrations, the labor force population, housing size, and altitude. Notably, the first three variables exhibited negative correlations, while the rest demonstrated positive correlations. The impact of oxygen concentrations on household energy consumption was particularly significant, as demonstrated by the notable correlation. In this study, we elucidated the intricate structure and determinants of energy consumption structure within herders.

Research on the Effects of Voltage Sags and Harmonics on the Output Characteristics of Switching Power Supply

Switching power supply is widely used in industrial equipment, household appliances, and other fields due to the advantages of high integration and lightweight. Voltage sags can seriously affect the output voltage of switching power supply and the working condition of its power supply equipment; at the same time, the working scenarios of power equipment using switching power supply often have different level of harmonic problems. Considering the continuous innovation of switching power supply technology, the previous analysis and test results on the effect of voltage sags and harmonics on switching power supplies may no longer be applicable. The paper reveals the mechanism of the effect of harmonic alone as well as voltage sag superimposed harmonics on the output voltage of the switching power supply. Then the test research program was developed, and four different brands switching power supplies were selected for the test research, with which the effects of different amplitude voltage sags, harmonics with different frequencies and distortion, as well as voltage sag superimposed harmonics on the output voltage of switching power supplies were investigated. The results show that the tolerance ability of switching power supplies to voltage sags has been improved compared with previous switching power supplies, and the harmonic itself has less effect on the output voltage of switching power supply, but the joint action of harmonics and voltage sags will have a greater effect on the output voltage of switching power supply. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Smart Homes, Smart Choices: Using Big Data to Boost Energy Efficiency and Environmental Sustainability

The major contributor to home energy consumption is home appliances, comprising about two-thirds of the total energy used. Analyzing electricity usage at the appliance level is crucial due to varying energy consumption levels among different appliances. This analysis allows individuals to either upgrade or minimize the use of inefficient appliances. To prioritize energy conservation, reducing the usage of energy-inefficient household appliances is imperative. Smart meters in a smart home scenario record electricity consumption data. The collected and monitored data from smart meters integrate with Big Data, playing a substantial role in this domain. Our proposed model optimizes electricity usage by identifying and categorizing household energy-inefficient appliances with a novel transition detection algorithm and appliance detection model. Furthermore, Utilizing Appliance-Level power consumption data from the UK-DALE dataset and One vs Rest clustering techniques, the proposed model effectively identifies devices with higher energy consumption. Additionally, the result analysis of the proposed model highlights the advantages of transitioning to energy-efficient devices, reducing both power consumption and financial wastage.

Household energy behaviour in the times of crisis: Lessons for policy initiatives from peripheral, fossil-dependent regions of the European Union

Drawing on the survey research conducted on a representative sample of 1200 Polish households living in peripheral, fossil-dependent regions, this paper explores the hitherto unrecognised changes in energy behaviour in the residential sector, which resulted from the increase in energy prices that initiated in 2022 after the Russia's invasion on Ukraine. We consider only no-tech and low-tech sustainable energy practices that meet the needs of households and deliver lasting reductions in domestic energy use and the increase of their energy efficiency. We concentrate on final energy consumption, i.e. energy behaviour related to heating houses, washing oneself and washing dishes, washing and drying clothes, as well as using TV, computers, household appliances and home lighting. We analyse the frequency of use of low-tech and no-tech practices and indicate the socio-economic and demographic characteristics of households and home features that differentiate them. In addition, applying the method of association rules, we outline key traits of household exhibiting different types of energy behaviour, namely ‘avoid’, ‘shift’ and ‘improve’. We use this knowledge to formulate implications for policy initiatives at European, national and local levels aiming at increasing the inclusiveness of the energy transition process.

A study on the characteristics and geometric optimization of a cyclone filter

Cyclone separator without filter is a common device used for separating solid–liquid/gas–solid/gas–liquid mixtures, which is widely used in chemical and household appliance industries. It has the advantages of high separation efficiency and low susceptibility to clogging. However, its separation efficiency cannot reach 100%, and some particles that enter the separator are not separated and will cause secondary pollution to downstream fluids. Combining the cyclone separator with a mesh filter to form a cyclone filter can achieve the complementary advantages of both, namely, high filtration efficiency and low susceptibility to clogging. For cyclone filters, particle shape has a significant impact on filter clogging, but there are few reports on this issue in the literature. In this paper, a new type of cyclone filter is proposed, and experimental and simulation methods are used to study its resistance characteristics. It is found that compared with the case of no filter, the flow resistance of the cyclone filter is significantly reduced, and the reason for the reduction in resistance is analyzed based on flow field analysis. The movement trajectory of flat and spherical particles in the cyclone filter and the problem of particle clogging on the filter are then studied. It is found that flat particles are more likely to cause clogging on the filter, and the reason for the clogging of the filter in the inlet area of the cyclone filter and the mechanism by which flat particles are more likely to clog the filter in the inlet area are analyzed based on the flow field and particle force in the separator. Finally, the inlet profile of the cyclone filter is optimized, and the changes in the inlet flow field and particle movement trajectory before and after optimization are compared. The problem of flat particle clogging on the filter is ultimately solved.

Sustainable EnergySense: a predictive machine learning framework for optimizing residential electricity consumption

In a world where electricity is often taken for granted, the surge in consumption poses significant challenges, including elevated CO2 emissions and rising prices. These issues not only impact consumers but also have broader implications for the global environment. This paper endeavors to propose a smart application dedicated to optimizing the electricity consumption of household appliances. It employs Augmented Reality (AR) technology along with YOLO to detect electrical appliances and provide detailed electricity consumption insights, such as displaying the appliance consumption rate and computing the total electricity consumption based on the number of hours the appliance was used. The application utilizes Linear Regression as a machine learning (ML) algorithm to develop the electricity consumption forecasting model for the next months, based on past utility bills. Linear regression is often considered one of the most computationally lightweight ML algorithms, making it suitable for smartphones. The application also offers users practical tips for optimizing their electricity consumption habits.

Considering product life cycle characteristics and industry background in environmental impact analysis and application: a case study of a television.

Life cycle assessment (LCA) is widely used to evaluate product's life cycle environmental impact and identify the environmental weaknesses. However, it is difficult for existing LCA software to perform flexible LCA analysis based on the product life cycle characteristics and industry background. Meanwhile, under the existing LCA research model, product designers and manufacturers are usually not LCA evaluators, resulting in a certain time gap between the evaluation results and product improvement. Designers with less experience in green design often find it difficult to identify high environmental impact links in products at different life cycle stages and product levels, and updated products are challenging to meet various environmental restrictions. This paper establishes a multi-module product life cycle analysis model that combines product industry background that includes basic information, assessment information, structural information, and restriction information to achieve the multi-scenario of product LCA in different dimensions in a typical domain. The calculated mechanism of the dynamic power emission factor is built according to the service time and space dimensions. The proposed method forms an integrated environmental performance evaluation of household appliance (EPEHA) system. A software assessment and an optimization method are proposed to improve the EPEHA system. The results of this study show that these proposed methods can improve the timeliness and diversity of results analysis of product LCA in the field of household appliances in China. The universal data exchange format and simple operation interface of the EPEHA system enable people related to the product to quickly understand the environmental impact of the product in different scenarios, even if they lack green design knowledge and professional software training.

From Sensors to Insights: An Original Method for Consumer Behavior Identification in Appliance Usage

In light of the energy crisis, extensive research is being conducted to enhance load forecasting, optimize the targeting of demand response programs, and advise building occupants on actions to enhance energy performance. Cluster analysis is increasingly applied to usage data across all consumer types. More accurate consumer identification translates to improved resource planning. In the context of Industry 4.0, where comprehensive data are collected across various domains, we propose using existing sensor data from household appliances to extract the usage patterns and characterize the resource demands of consumers from residential households. We propose a general pipeline for extracting features from raw sensor data alongside global features for clustering device usages and classifying them based on extracted time series. We applied the proposed method to real data from three different types of household devices. We propose a strategy to identify the number of existent clusters in real data. We employed the label data obtained from clustering for the classification of consumers based on data recorded on different time ranges and achieved an increase in accuracy of up to 15% when we expanded the time range for the recorded data on the entire dataset, obtaining an accuracy of over 99.89%. We further explore the data meta-features for a minimal dataset by examining the necessary time interval for the recorded data, dataset dimensions, and the feature set. This analysis aims to achieve an effective trade-off between time and performance.