Electrical Appliances Research Articles

Exploring women’s visceral engagement with electric appliances in Turkish kitchens

This paper investigates the narratives and experiences of women regarding cooking with small electric appliances. It intends to offer a novel perspective on gender and technology studies by foregrounding the visceral dimensions of these encounters. Drawing from a larger project on the historical representations and lived experiences of domestic technologies in Turkey, it highlights how the embodied dimensions of cooking shape the ways women perceive, adapt, and integrate technology into their daily lives. This study is based on interviews with twenty-seven women across five cities in Turkey conducted between 2022 and 2024. While small electric appliances are often marketed for convenience and efficiency, we argue that focusing solely on their instrumental benefits neglects the complex and visceral ways women engage with technology. A visceral approach remains an undervalued lens for understanding these interactions, particularly as women’s embodied knowledge and relationships to kitchen appliances challenge scholarship that prioritizes progress and efficiency. As active agents, many women resist these technologies, viewing them as misaligned with the embodied knowledge and practices integral to cooking. By reevaluating the relationship between food, gender, and technology, we propose that such disengagement challenges the positivist reliance on science and technology, emphasizing the importance of embodied knowledge and everyday practices in shaping women’s interactions with technology.

Identification of factors influencing electricity consumption in Yaoundé City, Cameroon

This study explores the factors influencing household electricity consumption in Yaoundé, Cameroon, where energy poverty, frequent power cuts and high tariffs pose major challenges to ensuring reliable, affordable and permanent access to electricity for populations, given the energy potential the country enjoys. Using a multinomial logistic regression model, the analysis reveals that the number of occupants, rooms, size of dwelling and number of electrical appliances have a significant impact on consumption, particularly in the low and medium consumption segments. The model explains 62% of the variation in consumption, leaving 38% unexplained, suggesting the need to explore other factors such as energy behaviour. The study recommends various measures to improve energy efficiency, including banning the import of second-hand appliances, encouraging the purchase of new appliances through tax adjustments, and awareness-raising campaigns targeted by consumption segment. For low-consumption households, the focus could be on adopting energy-efficient appliances, while for high-consumption segments, demand reduction at peak times could be encouraged. These strategies aim to improve energy efficiency and electricity conservation. Although focused on Yaoundé, this study could be applied to other cities in Cameroon and sub-Saharan Africa facing similar challenges, thus promoting an integrated approach to strengthening regional energy sustainability.

Development of hybrid CNN-LSTM for non-intrusive load monitoring

Non-Intrusive Load Monitoring (NILM) is a technique used to distinguish the energy consumption of individual electrical devices from aggregated energy consumption data without requiring additional sensors on each device. This technology plays a crucial role in efficient energy management, reducing energy costs, and supporting the development of smart buildings. This research focuses on developing a hybrid deep learning network to enhance NILM efficiency by combining convolutional neural networks with long short-term memory networks. This combination enables the analysis of complex electrical power signals, improving the accuracy of device classification, reducing prediction errors, and enhancing learning efficiency from diverse data. The proposed method is trained using electrical appliance interaction data in three configurations: 2-appliance, 3-appliance, and 4-appliance interactions. Experimental results demonstrate training accuracies of 98.59%, 98.59%, and 93.09%, respectively, while the highest testing accuracies are 98.59%, 95.61%, and 92.94%. These results highlight the potential for further advancements in NILM technology, enabling more efficient energy monitoring systems and promoting sustainable energy use in the future.

Exploring the acquisition, usage, repair, and end-of-life management practices of electrical self-care appliances: insights from domestic and professional users

Exploring the acquisition, usage, repair, and end-of-life management practices of electrical self-care appliances: insights from domestic and professional users

FePc coupled with FeNi/WxC embeded in leaf like multistage pore carbon as highly efficient bifunctional electrocatalyst for long life zinc-air battery

FePc coupled with FeNi/WxC embeded in leaf like multistage pore carbon as highly efficient bifunctional electrocatalyst for long life zinc-air battery

Design and Development of Internet of Things (IOT) - Based Automation System for Smart Home

Home Automation System gains popularity due to communication technology advancement. Smart home is one of the Internet of Things applications that facilitates the control of home appliances over the Internet using automation system. This paper proposes a low-cost Wi-Fi based automation system for Smart Home in order to monitor and control home appliances remotely using Android-based application An Arduino UNO microcontroller provided with Wi-Fi module is utilized to build the automation system. In addition, several sensors are used to monitor the temperature, humidity and motion in home. A relay board is exploited to connect the HAS with home under controlled appliances. The proposed automation system, can easily and efficiently control the electronic or electrical appliances using Wi-Fi and Virtual Mobile Application.

Adaptive Automation System

The paper "Adaptive Automation: Integrated Smart System with HiveX App" focuses on creating a robust automation solution that enhances energy efficiency and operational control. The HiveX App is at the heart of this system, providing a userfriendly platform for real-time management and monitoring of various smart systems. Through the app, users can seamlessly interact with different devices and systems, optimizing their operations to conserve energy and improve overall efficiency. By leveraging real-time data from sensors and automation controls, the HiveX App ensures that electrical appliances, lighting and environmental systems are activated only when necessary. This dynamic approach significantly reduces power consumption, as devices are automatically adjusted based on occupancy and environmental conditions. Users can remotely control and monitor their systems, making energy management more convenient and efficient. The app will give real-time insights to energy usage, which allows users to track and optimize consumption patterns. Its adaptability ensures that the automation system remains responsive to changing conditions, making it an essential tool for maintaining energy efficient, smart environments in homes and businesses.

Droplet-Based Triboelectric Nanogenerators with Needle Electrodes for Efficient Water Energy Harvesting.

With the advent of droplet-based triboelectric nanogenerators (D-TENGs), methods for converting raindrop kinetic energy to electrical energy have developed rapidly. However, current D-TENG designs suffer from slow solid-liquid interface separation speeds and susceptibility to liquid residues. These issues compromise the output performance of D-TENGs and limit their applications in high-power electrical appliances. To address this, this study presents a needle electrode droplet-based triboelectric nanogenerator (NED-TENG). The needle electrode functions as the top electrode, optimizing solid-liquid contacts and efficiently harvesting raindrop kinetic energy by leveraging the triboelectric and electrostatic induction mechanisms. This needle electrode is made from one end of a copper wire, with its other end directly connected to the energy harvester. This setup positions all of the wiring on the back of the substrate, accelerating liquid separation, mitigating residue formation, and simplifying device fabrication. Upon assembly of the device, several factors influencing the performance of the fabricated D-TENG and its action mechanisms are explored to improve its output efficiency. Experimental results reveal that the designed D-TENG only requires 6 s to saturate the surface charge of its polytetrafluoroethylene film, achieving a short-circuit current (ISC) of up to 4.76 mA and an output voltage (V0) of up to 563 V. Overall, this study offers a straightforward and effective approach for harvesting kinetic energy from rainwater.

Research on the Protection Performance Testing Technology of Explosion-Proof Electrical Appliances in Mines

Research on the Protection Performance Testing Technology of Explosion-Proof Electrical Appliances in Mines

Bimetallic Effect of Hybrid Flame Retardant on Flame Retardant Performance in Polypropylene.

Polypropylene composites are widely used for their excellent properties, but poor flame retardance is a major concern. The phosphorus content of the material in intumescent flame retardant (IFR) has a direct effect on retarding smoke and dehydrating to carbon during combustion. This study presents a hybrid flame retardant (MPA@ZrP-Zn) synthesized by noncovalent bonding between melamine and phytic acid molecules, containing a large number of phosphorus atoms in the system. Theoretical calculations confirm that the structure formed by zinc ions and melamine phytate (MPA) is more stable, and the overall electrophilicity index is at the level of a moderate Lewis acid. In addition, the Zr-Zn bimetallic effect significantly improves the drip resistance of IFR and reduces smoke emissions during combustion. UL-94 and LOI tests have shown that PP composites with MPA@ZrP-Zn (PPZn30) can achieve a V-0 rating and 33.5% of the LOI value. The THR and PHRR were 15.1% and 54.2% lower, respectively, for PPZn30 (compared to pure PP), and the ID/IG curves further indicate a higher degree of graphitization of the carbon layer in the combustion residue, which should be ascribed to the bimetallic effect. Therefore, this work puts forward a strategy to achieve high-performance PP composites with excellent flame retardant properties, which exhibit huge applications in electrical appliances and energy vehicles.

Psychological Determinants in Influencing Residents Adoption Intention of Home Energy Efficiency Measures: A Case in East Malaysia

In recent years, energy demand has been growing fast, primarily driven by global population growth. The residential sector is a major contributor to rising energy needs, accounting for 20.5% of Malaysia’s energy consumption. A substantial portion of domestic energy is used for electrical appliances, water heating, cooling and lighting, all of which contribute to increased greenhouse gas emissions. To reduce energy demand, carbon emissions and energy costs at both household and national levels, energy efficiency measures can play a crucial role. However, the factors influencing the adoption of these measures remain unclear within the Malaysian context. This study aims to explore the significance of psychological determinants in influencing residents' intentions to adopt home energy efficiency measures. Grounded in the Theory of Planned Behavior, a theoretical framework was developed, incorporating an additional variable to assess its potential impact on residents' intentions to adopt home energy efficiency measures. A systematic literature review identified four key psychological determinants—attitude, subjective norm, perceived behavioral control and past experience—as influencers of residents’ intentions to adopt energy efficiency measures. A questionnaire survey was conducted among 286 residents in Sibu, Sarawak. Frequency analysis was used to examine demographic information, and Partial Least Squares Structural Equation Modelling (PLS-SEM) was employed to analyze relationships within the model. Findings revealed that all determinants, except subjective norm, significantly influence households’ intentions to adopt home energy efficiency measures. The results provide insights for marketers and policymakers to shape targeted strategies, fostering a more sustainable society.

Wireless passive sensor design based on a highly stable triboelectric nanogenerator for centralized command of diverse electrical appliances

Wireless passive sensor design based on a highly stable triboelectric nanogenerator for centralized command of diverse electrical appliances

PENERAPAN ALGORITMA DECISION TREE UNTUK KLASIFIKASI KONSUMSI ENERGI LISTRIK RUMAH TANGGA DENGAN PENGGUNAAN RAPIDMINER

The research aims to explore and understand energy consumption patterns in households. By using the Decision Tree algorithm, to classify the level of electrical energy consumption. And data on household electrical energy consumption can be obtained from various sources, such as Household electricity meter. Survey or questionnaire filled out by homeowners regarding the use of electrical appliances. Based on the image above, the application of the Decision Tree algorithm in analyzing risk factors for The classification of household electrical energy consumption produces an accuracy value of 100.00%. From the displayed confusion matrix, we can see the distribution of predicted and actual values for various classes. For example, in the class "true 110 25," there are 17052 correct predictions. The evaluation results also show the precision and recall values for each class. The highest precision was achieved in the "true 2205" class with 100% recall, while the precision was found in the "true 122.5" class of 100.00%.

Smart homes energy management: Optimal multi-objective appliance scheduling model considering electrical energy storage and renewable energy resources.

Smart homes energy management: Optimal multi-objective appliance scheduling model considering electrical energy storage and renewable energy resources.

A non-intrusive load monitoring data generation method

Abstract Non-intrusive load monitoring (NILM) is a technique that does not require access to the interior of the system or to the interior of each electrical appliance. Instead, it only installs monitoring equipment at the entrance of the user bus. In the course of investigating non-intrusive load identification techniques, it is often necessary to collect extensive user load data in order to confirm the applicability of the proposed load identification methods across various situations. This requirement inevitably entails a significant burden of data collection and organization. In order to overcome this challenge, a hybrid approach was proposed that combines the principle of Frequency Invariant Transformation for Periodic Signals (FIT-PS) with Time Series Generative Adversarial Networks (TimeGAN). This method was employed to generate single-loaded waveform and multi-loaded waveforms, in order to meet the test requirements.

Implementation of Web-based Electrical Appliance Installation Monitoring: TAM Testing Approach

Implementation of Web-based Electrical Appliance Installation Monitoring: TAM Testing Approach

Unearthing the reality of ‘Zombie energy systems’ in Africa’s energy transition

Abstract Africa’s energy transition is unique and complicated by—among other things—the pervasive presence of ‘Zombie Energy Systems’ (ZES). These are outdated, inefficient, and environmentally harmful energy systems that persist despite being obsolete (e.g. old and mismanaged electrical grid systems and obsolete electrical appliances). These ZES hinder Africa’s energy transition, yet they receive limited attention in the international energy development arena. Rooted in colonial-era legacies, economic constraints, and policy shortcomings, these energy systems have led to a persistent misalignment between energy supply and development goals, perpetuating energy poverty and limiting economic growth. In this piece, we identify ZES as a critical barrier to Africa’s energy transition, arguing that addressing it requires more than just infrastructure replacement. A holistic approach is needed—one that includes decommissioning outdated assets, modernizing grids, promoting energy efficiency, improving regulatory environment and fostering local expertise in modern energy systems. We advocate for a comprehensive reevaluation of energy policies and investment strategies across Africa, emphasizing the importance of balancing the development of new energy systems with the critical need to upgrade and enhance existing inefficient energy systems. Neglecting the improvement of current energy systems risks undermining the effectiveness of Africa’s energy transition. Furthermore, we highlight the necessity of implementing tailored solutions that address the distinctive energy challenges faced by each country on the continent.

Synergistic Non-Intrusive Load Monitoring: Dual-Model Training and Inference for Improved Load Disaggregation Prediction

Load disaggregation is the process of identifying an individual appliance’s power demand within aggregate electrical load data. Virtually all recently proposed disaggregation methods are based on neural networks, thanks to their superior performance. Their achievable accuracy is, however, often limited by the quality of the data that have been used to train the underlying neural networks. In particular, if electrical appliances exhibit vastly differing temporal schedules and operational modes, their heterogeneous power consumption data can poison the disaggregation models and thus lead to a degraded overall accuracy. This paper presents a novel load disaggregation approach that relies on the use of multiple disaggregation models. We use a clustering method to partition training data by their power consumption patterns, allowing our approach to train separate models for appliance data exhibiting similar electricity consumption patterns. During disaggregation, our scheme selects the model that is closest to the appliance’s power consumption pattern, thereby leveraging the most fitting model for each specific instance. We demonstrate how our method surpasses the accuracy of the individual models designated for each cluster, while simultaneously improving upon the baseline model’s performance. When applied to the widely used ECO dataset, our approach achieves an average improvement of 13% in disaggregation accuracy over the use of a single baseline model.

Factors Affecting Domestic Electricity Usage with Reference to Colombo, Gampaha, and Rathnapura Districts, Sri Lanka

Currently, Sri Lanka is facing a major economic crisis. In the face of this economic crisis, it is clear that the government has failed to provide the necessary facilities for individuals to carry out their daily activities properly. It has become a challenge for the institutions as well as the industrial sector to carry out their work in an atomic manner through the daily electricity disruptions. Due to the inefficiency of electricity consumption in Sri Lanka, the amount of electricity wasted annually is very high. Therefore, the objective of this study was to find factors affecting electricity consumption in domestic applications in order to reduce the wastage of electricity. For achieving the objective, three cities of Colombo, Gampaha, and Rathnapura were selected using the cluster sampling method. Accordingly, the total sample was 75. A questionnaire and interview methods were used to collect data. Principal Component Analysis and Multiple Linear Regression Analysis were used for the data analysis. The findings revealed that the number of family members, the size of the house, the income, the use of Florence bulbs, living in a rented house, and the use of electrical appliances contribute to the increase in household electricity consumption. Considering those factors that are affecting domestic electricity consumption, the government, focusing on sustaining electricity consumption and providing necessary facilities to consumers, should take necessary steps.

The Impact of Salesperson’s Functional and Relational Customer Orientation on Customer Outcomes

This study aims at exploring the impacts of salespeople’s functional and relational customer orientation on customer outcomes as a whole construct consisting of customer satisfaction and customer loyalty. Research data were gathered using two samples of customers of electric home appliances as customers of search products, and perfume retailing as customers of experience customers. Following a convenience sampling techniques using an electronic 5-point Likert scale, a total of 391 and 426 responses were collected from sample 1 and sample 2, respectively. Using SmartPLS software, the results indicate that both salesperson’s functional and relational behaviors had significant effects on customer outcomes. However, the functional behavior had higher effect in search products case whereas the effect of salesperson’s relational behavior is larger for experience products. This study fills a gap in the literature on such effects using local samples of customers, and its contribution is that it instructs researchers and practitioners that customer outcomes are affected by material factors such as industry type and immaterial factors such as salesperson’s behaviors.