Electric Home Appliances Research Articles

Monitoring Tool for Electricity Usage in Residential Homes Based on the Internet of Things (IoT) Using BLYNK Application

Electricity is a basic need of human beings. The consumption of electricity usage is getting bigger and bigger. Electricity usage is usually wasteful due to lack of public awareness and ineffective use of electricity. With this problem, the author aims to design loT-based electric power usage monitoring system in home electrical appliances. This tool is designed using sensors to detect the value of the voltage and current of the load attached to the electricity but still using the BLYNK application as a server and application to monitor the use of electric power through a smartphone application. The measurement results include the data of current, voltage, and electrical power used. This system can also remotely switch appliances ON/OFF. The test was conducted by comparing the results of the power value from the power meter with the power value in BLYNK application. After the test, the results of the comparison between the monitoring tool and the power meter measuring instrument obtained an average power error value of 0.5%. So that the accuracy value of the tool can also be 99.5%. With a large accuracy value, it means that the tool works well and can be used to monitor the use of electrical energy in households.

Enhancing smart home appliance recognition with wavelet and scalogram analysis using data augmentation

The development of smart homes, equipped with devices connected to the Internet of Things (IoT), has opened up new possibilities to monitor and control energy consumption. In this context, non-intrusive load monitoring (NILM) techniques have emerged as a promising solution for the disaggregation of total energy consumption into the consumption of individual appliances. The classification of electrical appliances in a smart home remains a challenging task for machine learning algorithms. In the present study, we propose comparing and evaluating the performance of two different algorithms, namely Multi-Label K-Nearest Neighbors (MLkNN) and Convolutional Neural Networks (CNN), for NILM in two different scenarios: without and with data augmentation (DAUG). Our results show how the classification results can be better interpreted by generating a scalogram image from the power consumption signal data and processing it with CNNs. The results indicate that the CNN model with the proposed data augmentation performed significantly higher, obtaining a mean F1-score of 0.484 (an improvement of +0.234), better than the other methods. Additionally, after performing the Friedman statistical test, it indicates that it is significantly different from the other methods compared. Our proposed system can potentially reduce energy waste and promote more sustainable energy use in homes and buildings by providing personalized feedback and energy savings tips.

Home Automation Using Smart AI Assistant

Artificial intelligence is taking over all the platforms and applications and making them more and more user friendly and intelligent and gives the application a human like behaviour and thinking. AI is being used everywhere now-a-days because applications that use AI are able to learn and improve and require less maintenance. This paper “Home automation using smart AI assistant” is a voice command-based AI assistant coded using python and Natural Language Processing NLP that can respond to user queries and responds to voice-based commands of the user. Using this AI Assistant users can send emails to anyone just by speaking the receiver’s name, subject of email and the email body. The users can send WhatsApp messages to any of their contacts just speak the message and to whom you want to send it, users can do google searches and open google tabs by voice search, users can do Wikipedia searches and can also open any YouTube video they want to watch. With this AI Assistant we can also get weather updates and news updates, just speak what news updates you want and the Assistant will read it for you. We can also check the computer performance, add remainders and to-dos. We can open our documents, play our favourite music or watch our favourite movie and do many more thigs just by a simple voice command. This assistant is also used for voice-controlled home automation. Using this Assistant, we can control our home electrical appliances with simple voice commands like turn on the room lights, fans, AC etc.

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.

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.

Reduction in Residential Electricity Bill and Carbon Dioxide Emission through Renewable Energy Integration Using an Adaptive Feed-Forward Neural Network System and MPPT Technique

Increasing electricity demand and the emergence of smart grids have given home energy management systems new potential. This research investigates the use of an artificial neural network algorithm for a home energy management system. The system keeps track of and organizes the use of electrical appliances in a typical home with the objective of lowering consumer electricity bills. An artificial-neural-network-based maximum-power-point-tracking scheme is applied to maximize power generation from photovoltaic sources. The proposed neural network senses solar energy and calculates load requirements to switch between solar and grid sources effectively. The implementation of improved source utility does not require numerical calculations. Traditional relational operator techniques and fuzzy logic controllers are compared with the suggested neural network. The model is simulated in MATLAB, and the results show that the artificial neural network performs better in terms of source switching following load demand, with an operating time of less than 2 s and a reduced error of 0.05%. The suggested strategy reduces electricity costs without affecting consumer satisfaction and contributes to environmental friendliness by reducing CO2 emissions.

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.

Artificial Intelligence Based Chatbot for Appliance Control

Now a days, numerous uses of artificial intelligence are found in the healthcare industry. Building advanced devices that can detect diseases and identify cancer cells uses AI applications. In order to ensure early diagnosis, artificial intelligence can assist in analyzing chronic illnesses with lab and other medical data. In this study, an IoT based home management and monitoring system with cheap cost and flexibility is presented. Using the Facebook Messenger app, users may access and remotely operate gadgets and appliances using smart phones. In contrast to prior systems, the proposed system provides a communication protocol to monitor and control the home appliences with the Messenger app in smart phones. The intelligent networking of common place devices like smart phones, Internet TVs, sensors, and actuators to the Internet is known as the Internet of Things (IoTs). New forms of communication between objects and humans as well as between things and other things are made possible by this. Now that everyone, from anywhere, can have connectivity for anything, In this project, the Facebook Messenger and the ESP8266-12E NodeMCU are used to operate electrical home appliances. Chatfuel is the tool used to build Facebook AI chatbots. IFTTT and Adafruit IO are also used by the project for back-end assistance.

The Determination of Customer Loyalty Factors on Electrical Home Appliances Brands Choices in Sarawak

Customer loyalty is about appealing to the right people, getting them to buy often and in larger quantities. Although many studies on loyalty have been done (Bambang et al., 2023; Huang, 2009), however, studies on determining the factors affecting the customer loyalty still needs further attention. 1971 respondents were selected for the purpose of this study. The respondents were selected from several major cities in Sarawak. This study identified that there are eight important elements generally affecting customer loyalty on electrical home appliances brands choices. Using factor analysis, the components are grouped into factors given; country of origin (COO), brand equity (BE), satisfaction (SAT), affect, resistance to change (RTC), value, trust, and corporate reputation (CR). The findings also suggested that Malaysian have low ethnocentric tendencies and does not stress on cultural differences. In rank order, it was found that the most important factor contributing to customers’ loyalty are SAT followed by CR, COO, BE, trust, RTC, value and affect factors respectively.

THE DUAL EFFECT OF FEAR OF PANDEMIC ON CONSUMERS (SATISFACTION AND REPURCHASE INTENTION) FOR ELECTRICAL HOME APPLIANCES DURING COVID-19

Electrical Home Appliances (EHA) are necessary for modern life. Some of these items are costly. Customer needs cognitive decision before purchasing. COVID-19 and its impact on humanity are disastrous and devastating (lockdown, social distance, joblessness, low/no income, death, and long health crisis). Like other businesses, EHA purchases and their usage have been affected. The study investigated customer satisfaction and the repurchase intention of EHA. The study was conducted in Malaysia and data were collected from 400 Malaysians using structured questions through social media and emails. Collected data were analysed with PLS-SEM (SmartPLSv.3). The study found that product quality, customer perceived value, and social media usage were significant with customer satisfaction and customer satisfaction and fear of the pandemic were also influential on repurchase intention. Besides, customer satisfaction is mediated fully with repurchase intention. Moreover, the contributory part is to investigate the moderating effect of fear of the pandemic on the relationship between satisfaction and repurchase intention was significant. Fear of the pandemic was a successful exogenous and interaction construct. The study used two prominent theories to support the framework and the relationships: Stimulus Organism Response theory and Perceived Risk Theory were successfully buttressed by current findings. These findings were very insightful for practitioners and knowledge-builders.

Optimizing energy consumption patterns of smart home using a developed elite evolutionary strategy artificial ecosystem optimization algorithm

In recent times, the tremendous progress in electronic devices and making them available to all consumers has led to an increase in demand for energy, the formation of a peak, and an increase in the electricity bill of the consumer. Buildings are considered one of the main sectors of energy consumption. Improving the energy efficiency of these buildings will achieve economic and environmental goals. This paper proposes a modified algorithm for scheduling electrical appliances in the smart home to reduce the electricity bill, improve the network, maximize customer comfort and reduce peak formation. This paper uses load conversion to change the shape of the load, as it is one of the types of demand side management (DSM) strategies. A modified algorithm, called elite evolutionary strategy artificial ecosystem-based optimization (EESAEO), is used in scheduling household appliances. The obtained results using the EESAEO algorithm reduce the electricity bill, improve the network, maximize customer comfort and reduce peak formation. The original algorithm is used to compare the simulated results and to verify the strength of the proposed EESAEO algorithm. Different price strategies such as TOU, CPP, and RTP are used, to ensure the efficiency of the studied system. These successful results prove that the applied method can be used for scheduling household appliances.

Telegram Application for Monitoring, Controlling and Protecting the Consumption of Household Electrical Appliances

Nominal electricity rates always increase, so it is necessary to take steps to save electricity consumption. One of the steps to save electricity consumption is taken by implementing a remote electrical equipment control system based on the Smart Home Internet of Things (IoT) system. Besides being able to save money, IoT systems are also able to help simplify work and human activities. This research aims to implement Internet of Things (IoT) equipment to monitor, control, protect, and On/Off timers for household electrical appliances monitored by the Telegram application and 16×2 LCD. Monitored system parameters i.e. voltage, current, frequency, power, energy, and electricity rates for each device. Device hardware i.e. Arduino Uno, 1 Channel Relay, PZEM-004t Voltage Sensor, NodeMCU ESP8266, and Current Sensor (Current Transformer). The equipment has been successfully tested on 10 different household appliances i.e. charged cellphones, fans, charged laptops, solder, iron, fluorescent lamp 36 W, television (TV), set-top box (STB)-TV, printer, and 5 W bulb lamp. The results showed that the prototype is capable of monitoring, controlling, protecting, and On/Off timer settings as well as being able to be monitored from home electric appliances both near and far using the 16×2 LCD and the Telegram application, respectively. The sensor tool using the Telegram application is able to measure/monitor the parameters of voltage, current and frequency at 10 loads with an average error value of 0.32%, 12.83% and 0.1% respectively compared to measurements using a Multimeter. The IoT prototype is also able to provide more complete, faster (4-5 sec) monitoring, control, protection, timer On/Off parameters and superior performance compared to devices designed by a number of previous researchers.

SMART HOME SECURITY APPLICATION

Protection of our personal properties is a key challenge and prior responsibility every time which affects massively on a day-to-day life. The current system of achieving security has plenty of drawbacks and less secure. The main goalof implementing a home security and automation system is to achieve apowerful and more secure way to handle the day-to-day stuff preventing from misuse hands and keeping track of usage of home electrical appliances to know the necessary and unnecessary actions. The system is built on IoT (Internet of things) to make more accurate and error-free control over the flow of the system. To make the difference from the existing system, in this proposed system we built the communication of hardware devices with an application where devices take commands and operate it while application rise the commands and keeps track of each transaction made so far. The combination of hardware and software will make life easier and saferfor its users.

Smart Home with Natural Language Interface Using Arduino

In the fast-paced life of the 21st century, computerization plays a crucial part in mortal life. Home computerization allows us to control home electrical appliances like lights, doors, AC, etc. It also provides home security and allows an emergency system to be activated. Home computerization refers to not only reducing fatal attempts but also increasing energy efficiency and saving time. The main idea of home computerization and security is to help handicapped and elderly people by enabling them to control home appliances and warn them in critical situations. This design focuses on the implementation of home robotization and security systems using an Arduino microprocessor and an Android smartphone. Home appliances are connected to the microprocessor, and communication is entrenched between the Arduino and an Android mobile device or tablet via the Bluetooth module. It promotes system authentication for a sanctioned person to pierce home appliances. KEYWORDS: Home Automation, Microprocessor, Arduino, Bluetooth, Android mobile, Handicapped.

Modelling and Simulation of Residential Load Profiles as an Approach for Data-Driven Prediction

Rapid growth of buildings energy consumption encourages to take measures to improve energy efficiency by actors involved in the field. One of the approaches developed last decades consists in energy management through energy prediction. These approaches engage machine learning algorithms, which focus on predicting energy consumption based on past-observed data. But there are also cases when this information is missing so in this paper, we focus on solving the problem when measured data are not available. Initially, we develop an electrical home appliance simulator, which reflects their energy consumption and occupant behavior. Each of the considered device is modelled using an electrical circuit analogy. Then aggregating single appliance energy consumption from simulator, total power consumption data is generated. Synthetic data are feed to an Artificial Neural Network algorithm to learn consumption pattern and to predict next hour energy consumption.

Remote Control of Electrical Home Appliances Using RF Module based Remote

This study examines the use of an RF-based remote system to manage domestic electrical appliances. The wireless remote control based on an RF technology can toggle the status of equipment to ON or OFF from any site without necessitating a line of sight anywhere around house. The regulating circuit is developed around a 434 MHz operational frequency RF receiver and transmitter module, along with the HT12E encoder and HT12D decoder integrated circuits and a few other passive components. The four distinct channels at the encoder end are used as switches for input signal, and the four channels at the decoder end are used as outputs points, which is connected to the respective electrical devices that are to be controlled; if the appliances are of a higher rating, the relays are used as an intermediary to connect the equipment to the receiving end of the circuit. The transmission method used in this instance is Amplitude Shift Keying (ASK), and the circuit is powered with a 9V DC battery source. The main objective of this task entails creating a closed circuit without any programming knowledge and making it functional without the need for line-of-sight utilizing RF technology. Also, as proposed development to the current working model is stated with a brief description of its functioning, which aims to efficiently control more than four electrical units using the same remote-control technology with the aid of a matrix keypad as input for switching the states of the devices, a matrix encoder & decoder for respective encoding & decoding of signals, and a stable multivibrator for circuit operations.

E-Waste Management in Malaysia: Residents’ Willingness to Pay for Household E-Waste Recycling in Kuching, Sarawak

‘E-waste’ refers to electronic and electrical appliances that are no longer beneficial to consumers. An increasing amount of e-waste generated due to an increasing demand for electrical and electronic equipment in line with the rapid growth of the global population has become a growing global concern. Furthermore, limitation of the assimilative capacity of the environment and depleting natural resources have also urged the need to prioritize sustainable household e-waste management. This paper aims to examine the resident’s willingness to pay for household e-waste recycling in Kuching, Sarawak. A questionnaire survey was administered among 397 residents living in Kuching. The findings of the study show that majority of the residents in Kuching are not willing to pay for household e-waste recycling of electrical appliances such as fridges (50.6%) and fans (59.4%). However, it also depends on the distinctive types of household electrical appliances they possess. In this study, 10 types of household electrical appliances were selected and the average cost of WTP is expressed based on a percentage of its purchased values of the respective electrical household items listed. It was found that residents from Kuching are only willing to pay an average of 2.3% till 2.6% of recycling fees based on 10 types of the purchased values of their electrical home appliances in this study. This could be explained by the findings that show a low awareness level of sustainable e-waste management practices among the residents in Kuching. Therefore, the study recommends that more environmental education programs are to be carried out in the formal education system to sensitize the residents and children to the risks associated with the importance of household e-waste recycling to promote sustainable household e-waste management, particularly among the growing population in Kuching, Sarawak. Chiam (2013) reported that the residents of Kuching lack knowledge of how to manage their household e-waste and are unaware of the available recycling facilities in the region. However, over time, the e-waste management situation in Kuching has improved, with more recycling facilities being made available to the public for proper disposal of e-waste (Louis, 2020). The Kuching Integrated Waste Management Park, which caters to municipal solid waste and scheduled waste (including e-waste), is claimed to be one of the first integrated waste management parks in Southeast Asia (Trienkens, n. d.).

A Brief Overview on Influence of Financial Factors on Stock Prices of Listed Company An Case Study of Chinese Home Electrical Appliance Industry

This study aims to investigate the effect of financial factors on stock prices, and find out which factors are most statistically significant to the change of stock prices. Based on the information of 16 listed companies in Chinese home electrical appliance industry, three simple linear models are built and compared. The final model suggests that earnings per share, operating income per share, debt-to-asset ratio are statically significant to a company’s stock price. Besides, intrinsic diversity between companies contribute to difference in their stock prices.

Driven by personal or environmental gains? Investigating consumer motives behind purchasing long-lasting products

This study investigates the psychographic antecedents of consumers' preference for product longevity when purchasing electrical home appliances. The motivational factors behind this preference are determined by using a conceptual model that combines Lancaster's consumer theory with theory of planned behaviour and theory of basic human values. The analysis is built on survey data collected from a population-representative sample of 499 German consumers. Using structural equation modelling, it is found that consumers' preference for a long product lifetime is fostered by a respective positive attitude, but only if it is driven by personal rather than environmental gains. Furthermore, biospheric values enhance both attitude types, while stimulation values inhibit them; hedonic values only enhance attitude based on personal gains. Therefore, business practitioners are advised to communicate the personal benefits of long-lasting electrical home appliances and promote business models for product longevity that account for product variety and up-to-dateness. Policymakers should further develop educational measures to strengthen biospheric values and pro-environmental attitudes towards product longevity in society.

Voice Control Smart Home Electrical Appliances for Visually Impaired Person

This paper proposed a voice control based smart home electrical appliances for visually impaired person. This voice control smart home system decreases the time taken for visually impaired person to control their home electrical appliances by giving specific voice command compared to traditional switch or remote control. Besides, the voice control smart home system also contained a mini speaker to provide a feedback audio and ensure the correct electrical appliances were controlled. The implementation of this voice control smart home system provides a more convenient and efficient way for visually impaired person to control their electrical appliances. An experiment was conducted by 50 participants to collect the data of the accuracy of the developed system with testing 100 times voice commands for each participants. The participants achieved 96.74 times correctly on average, with the accuracy 96.74% on this system. 79.33% of the participants agree or strongly agree that would use this system in the future for controlling their home electrical appliances.