Appliance Usage Research Articles

Research on User Interface Design of Home Appliances Based on Behavioural Logic

It is to solve the usability problem of user interface of home appliances under the development trend of home appliance automation, informatization and multi-function through the design of home appliance user interface dominated by user behavioral logic. It uses the methods of comparative analysis and empirical analysis, and it's distilled and compared the main difference between behavioral logic-based user interface and the functional logic-based user interface of home appliances, and then it summarizes and analyzes the main characteristics and advantages of behavioural logic-based user interface. The design ideas of behavioral logic-based user interface based on design psychology and other theories are provided, including that the behavioural logic-based user interface design of home appliances should be based on the user's mental model, and the user's cognitive experience of the task scene and its elements of decision-making process should be extracted, and the user interface should be organized according to the information of common operation scenes and the operation paths, and so on.

Analysing the Residential Electricity Consumption using Smart Meter

A massive amount of electricity usage may be accessed on an everyday and hourly basis due to the advancement of smart power measuring technology. Electricity demand management and utility load management are made easier by energy usage forecasts. The majority of earlier studies have concentrated on the power consumption of business clients or residential buildings, or they have experimented with individual household electricity usage using behavioral and occupant sensor information. This study used smart meters to examine energy usage at a single household level to enhance residential energy services and gather knowledge for developing demand response strategies.The power usage of various appliances in a single household is estimated, by utilizing Autoregressive Integrated Moving Average (ARIMA) modeling technique, which is applied to daily, weekly, and monthly information granularity. To select the household’s energy consumption dataset for this study, a multivariate time-series dataset describing the four-year electricity usage of a household is provided. The use of Exploratory Data Analysis (EDA) is utilizedfor the selection of features and data visualization. The correlation coefficients with the daily usage of the household have been computed for the characteristics prepared for the forecast. The top three major determinants with the top three positive significance are "temperature," "hour of the day," and "peak index." A single household's usage is inversely related to the variables having negative coefficients. It should be noticed that the correlations among a household's attributes with usage vary from one another. Finally, the power prediction is analyzed in a single household.

중국 소비자의 스마트 가전제품에 대한 만족도 및 지속적 사용의도 연구 : 기술수용모델을 중심으로

The smart home appliance industry is growing rapidly and the growth is predicted to continue with the development of science and technology. This can be attributed to the practical help they provide to users with convenience and an automatic control system. This study analyzed the influencing factors of satisfaction and continuous usage intentions of smart home appliance users living in Beijing and Shanghai, China. A total of 600 valid questionnaires were included in the final analysis.BR First, usefulness, practical value, trust, and product knowledge for smart home appliances showed a positive effect on satisfaction. Second, satisfaction with smart home appliances showed a positive effect on intentions to continuous usage. Third, the moderating effect of involvement analysis results showed that involvement had a significant positive moderating effect on the relationship between practical value and satisfaction with smart home appliances.

A Comprehensive Predictive-Learning Framework for Optimal Scheduling and Control of Smart Home Appliances Based on User and Appliance Classification.

Energy consumption is increasing daily, and with that comes a continuous increase in energy costs. Predicting future energy consumption and building an effective energy management system for smart homes has become essential for many industrialists to solve the problem of energy wastage. Machine learning has shown significant outcomes in the field of energy management systems. This paper presents a comprehensive predictive-learning based framework for smart home energy management systems. We propose five modules: classification, prediction, optimization, scheduling, and controllers. In the classification module, we classify the category of users and appliances by using k-means clustering and support vector machine based classification. We predict the future energy consumption and energy cost for each user category using long-term memory in the prediction module. We define objective functions for optimization and use grey wolf optimization and particle swarm optimization for scheduling appliances. For each case, we give priority to user preferences and indoor and outdoor environmental conditions. We define control rules to control the usage of appliances according to the schedule while prioritizing user preferences and minimizing energy consumption and cost. We perform experiments to evaluate the performance of our proposed methodology, and the results show that our proposed approach significantly reduces energy cost while providing an optimized solution for energy consumption that prioritizes user preferences and considers both indoor and outdoor environmental factors.

Aggregate demand response strategies for smart communities with battery-charging/switching electric vehicles

Demand response (DR) plays an important role in the peak shaving and load-shifting of a community, but the avalanche effect creates a huge obstacle in DR with uncoordinated usage of appliances and charging of electric vehicles (EVs). Recognizing the remarkably rising deployment of EVs, there is great potential to coordinate EVs' charging in a charging station or a battery switching station for providing DR. However, the potential benefits of the battery-switching EVs for providing DR and their impacts on the aggregate load of the community have not been fully studied. In this regard, the present study proposes two novel aggregate community-level DR strategies, i.e., a strategy employing battery-charging EVs and a strategy employing battery-switching EVs. The proposed strategies are compared with the conventional strategy that only incorporates community battery energy storage for enabling peak shaving. The cost optimization is conducted with optimization parameters including demand limits during different periods and the power and energy capacity of the battery energy storage system. A stochastic bottom-up demand model for households, a probabilistic model for predicting loads of EVs, and a redistribution time-of-use tariff are implemented in the optimization. A group with 20 households and 20 EVs in New York, USA is used in the case study. The results in the case study show that the total annual costs (electricity tariff cost and battery energy storage cost if any) are reduced by 63.3 %, 54.0 %, and 14.7 % under the battery-switching EV strategy, the battery-charging EV strategy, and the battery energy storage strategy compared to the base case (i.e., DR-free strategy), respectively. Additionally, the battery-switching EV strategy enables DR throughout the day with the maximum temporal flexibility. Last, the developed two EV strategies can provide a good stability of the household loads on the grid and avoid the avalanche effect.

Prevalence of usage of various removable appliances among undergraduate dental students in a private dental college.

The aim of the present study was to assess the prevalence of usage of various removable appliances among undergraduate dental students in private dental colleges. Ethical approval for the study was obtained from the Institutional Review Board of Saveetha Institute of Medical and Technical Sciences, Chennai, India. A dental record of patients was investigated. Data evaluation was completed and entered into Microsoft Excel. Gender distribution among the cohorts was assessed. The most commonly used appliance is Hawley's appliance (61), followed by Essix retainer (25), Begg's wrap-around retainer (23), Hawley's appliance with tongue crib (16), cantilever spring (7), splint (7), and T spring (1), respectively. The majority of the females (36) took Hawley's appliance. The difference was not statistically significant between both the arches and between the male and female population. Hawley's appliance is the most commonly used removable appliance among dental graduates.

Awareness on three-dimensional printing of orthodontic appliances among dental students.

The aim of the study was to evaluate the knowledge and awareness regarding the use of three-dimensional (3D)-printed appliances used in orthodontics among students pursuing dentistry. The distribution of the questionnaire was done using an online Google Forms link to about 100 dental students. The questionnaire compromised questions that were designed for assessing the various findings and knowledge of update, and finally, questions related to facts on various 3D-printing appliances. Among the total population, 58% of the population are aware of 3D printing used for various dental applications, whereas 42% of the population are not aware of the same. Among the total population, 58% of the population are aware of 3D printing used in dentistry, whereas 42% of the population are not aware of 3D printing used in dentistry. Inside the limits of the review, it very well may be inferred that 3D-printed machines have a rising use in the majority of the dental fields and understudies chasing after dentistry know about it. The knowledge about the basic and fundamental working and the usage of such appliances is developing among undergraduate students. Further continuing education programs can be provided to improve the same.

The Status of Smart Home Appliances Usage and Requirements for Improving Accessibility among People with Visual Impairment

The Status of Smart Home Appliances Usage and Requirements for Improving Accessibility among People with Visual Impairment

Energy usage and human behavior modeling for residential bottom-up energy simulation

We introduce a probabilistic modeling for a disaggregated Bottom-up simulation of residential energy usage. Parametric probability distributions are modeled with parameters that have a natural explanation in terms of usage and appliance power. Human behavior such as sleep and home occupancy variables are considered too, with its corresponding trained probabilistic Models. Model parameters are adjusted by the minimization of the Kullback–Leibler divergence from known appliance and behavior usage data. Self-generated photovoltaic Energy is included in the simulation with a battery for storage and electric vehicle usage. Simulations match individual and aggregated usage load profiles in the European REMODECE and RSE Italian load data sets. Obtained Models are useful for residential disaggregated simulations allowing individual appliances to change from house to house. Probabilistic distributions can be used as prior knowledge for energy management systems, risk management, and grid failure prediction and can be adapted based on non-stationary real-time house behavior and appliance usage.

Using Household Electricity Metrics in Identifying Trends in Residential Appliances Usage in Selected Ghanaian Homes

Using Household Electricity Metrics in Identifying Trends in Residential Appliances Usage in Selected Ghanaian Homes

The Italian Wellness Survey of More Than 1000 Ostomy Patients: Trends in Patient Adjustment After Stoma Placement

BACKGROUND: Despite technological advances to minimize the damage caused by ostomy surgery, patients face many physical and emotional challenges that can severely impact their health-related quality of life. PURPOSE: This survey monitored the wellness of patients with ostomies during their clinical follow-up through the 8-item Wellness Questionnaire, a simplified version of the validated Ostomy-Q. METHODS: The survey involved 1054 Italian novice and expert ostomy appliance users who had received a new device prescription (expert users changed to a new device due to problems with a previous device such as a change in weight or body profile and leakage). Patients were questioned at 2 time points: baseline (T0) and 4 weeks after stoma placement (T1). RESULTS: The overall Wellness Questionnaire score and the individual items generally improved from T0 to T1 for both types of users, suggesting that both novice and expert users face an adjustment phase for the ostomy device that impacts several aspects of their life. However, novice users were less concerned about social relations and skin irritation at both time points compared with expert users, whereas device visibility under clothes at T0 and social relationships at T1 were of lesser concern for experienced users than for novice ones. CONCLUSION: The Wellness Questionnaire, carried out by stoma therapists during the clinical follow-up of patients with ostomies, highlighted an overall improving trend 1 month after the device prescription for both expert and novice users. Both types of patients faced daily challenges related to the new ostomy device but from different perspectives. Further studies are needed to confirm the outcomes of this survey.

Real-time recommendations for energy-efficient appliance usage in households.

According to several studies, the most influencing factor in a household's energy consumption is user behavior. Changing user behavior to improve energy usage leads to efficient energy consumption, saving money for the consumer and being more friendly for the environment. In this work we propose a framework that aims at assisting households in improving their energy usage by providing real-time recommendations for efficient appliance use. The framework allows for the creation of household-specific and appliance-specific energy consumption profiles by analyzing appliance usage patterns. Based on the household profile and the actual electricity use, real-time recommendations notify users on the appliances that can be switched off in order to reduce consumption. For instance, if a consumer forgets their A/C on at a time that it is usually off (e.g., when there is no one at home), the system will detect this as an outlier and notify the consumer. In the ideal scenario, a household has a smart meter monitoring system installed, that records energy consumption at the appliance level. This is also reflected in the datasets available for evaluating such systems. However, in the general case, the household may only have one main meter reading. In this case, non-intrusive load monitoring (NILM) techniques, which monitor a house's energy consumption using only one meter, and data mining algorithms that disaggregate the consumption into appliance level, can be employed. In this paper, we propose an end-to-end solution to this problem, starting with the energy disaggregation process, and the creation of user profiles that are then fed to the pattern mining and recommendation process, that through an intuitive UI allows users to further refine their energy consumption preferences and set goals. We employ the UK-DALE (UK Domestic Appliance-Level Electricity) dataset for our experimental evaluations and the proof-of-concept implementation. The results show that the proposed framework accurately captures the energy consumption profiles of each household and thus the generated recommendations are matching the actual household energy habits and can help reduce their energy consumption by 2–17%.

Machine Learning-Based Load Forecasting for Nanogrid Peak Load Cost Reduction

Increased focus on sustainability and energy decentralization has positively impacted the adoption of nanogrids. With the tremendous growth, load forecasting has become crucial for their daily operation. Since the loads of nanogrids have large variations with sudden usage of large household electrical appliances, existing forecasting models, majorly focused on lower volatile loads, may not work well. Moreover, abrupt operation of electrical appliances in a nanogrid, even for shorter durations, especially in “Peak Hours”, raises the energy cost substantially. In this paper, an ANN model with dynamic feature selection is developed to predict the hour-ahead load of nanogrids based on meteorological data and a load lag of 1 h (t-1). In addition, by thresholding the predicted load against the average load of previous hours, peak loads, and their time indices are accurately identified. Numerical testing results show that the developed model can predict loads of nanogrids with the Mean Square Error (MSE) of 0.03 KW, the Mean Absolute Percentage Error (MAPE) of 9%, and the coefficient of variation (CV) of 11.9% and results in an average of 20% daily energy cost savings by shifting peak load to off-peak hours.

Patient perception of orthodontic services during the Covid-19 pandemic

In the field of dental and oral health services, orthodontists are one of the professions that have a high enough risk of being ex-posed to Covid-19. It is possible for each patient to transmit the SARS Cov-2 virus to orthodontists, other health workers and another patient. This study was aimed to determine patient perception to orthodontic clinic services and routine orthodontist vi-sits in orthodontic treatment during pandemic Covid-19. Methods: This study used adescriptive correlational, non-experimental study with a cross-sectional design. The sample of the study consisted of 134 orthodontic appliance users who had routine or-thodontic visits to orthodontic clinic around Denpasar City. Result. Based on the research, it is known that 132 orthodontic ap-pliance users (98,51% of the respondents) had a good perception of orthodontic clinic services during the Covid-19 pandemic and 117 orthodontic appliance users (87,31% of the respondents) had a fairly good perception of routine orthodontic treatment visits during the Covid-19 pandemic. In addition, the results of the Spearman’s rank correlation coefficient (Spearman’s ρ) test show a significance value of 0.065>0.05. It can be concluded that the orthodontic clinic services have had no impact on the routine orthodontic visits of orthodontic appliance users during pandemic Covid-19

Differences in dental and oral hygiene status between orthodontic and conventional toothbrush users in fixed orthodontic treatment

Good oral and dental hygiene is a challenge for orthodontic patients because food is easily trapped around brackets and under the arches of braces, thus becoming a barrier to brushing teeth. An effective way to get rid of plaque is to use a toothbrush regularly, to keep your teeth and mouth clean and healthy. This study was intended to determine the difference in dental and oral hygiene status between orthodontic and conventional toothbrush users in fixed orthodontic treatment.The results of the analysis of 6 articles are that the use of orthodontic toothbrushes is more effective than conventional toothbrushes in removing plaque. Or-thodontic toothbrushes can also provide a significant plaque reduction effect on fixed orthodontic appliance users. Orthodontic toothbrushes did not show any difference in removing plaque compared to conventional toothbrushes. It was concluded that there were differences in dental and oral hygiene status between orthodontic and conventional brush users in fixed orthodontic treatment.

Trends in appliance usage and electricity consumption in households: A case study in Hambantota District, Sri Lanka

Ruhuna Journal of Science (RJS) is a multidisciplinary Science Journal published by the Faculty of Science, University of Ruhuna. It welcomes articles resulting from original research work covering many areas in Pure and Applied Sciences, and Technology, and scientific articles with interdisciplinary focus.Ruhuna Journal of Science is indexed in Web of Science (Master Journal List/ Zoological Records), and included on DOAJ and World Catalogue of Scientific Journals (WCOSJ).To submit your manuscript please go to http://rjs.ruh.ac.lk/index.php/rjs/indexCover Page ImageYala (යාල) National Park is the most visited and second largest national park in Sri Lanka, bordering the Indian Ocean. It is situated in the southeast region of the country with 124 km distance towards east from University of Ruhuna. Including Sri Lankan elephant, 44 species of mammals are resident in Yala National Park, and it has one of the highest leopard densities in the world. Shown in the cover image is the Patanangala, a rock outcrop in the Yala beach.Source: https://en.wikipedia.org/wiki/Yala_National_Park

Sustainable Energy Management at KMUTT Thailand

King Mongkut’s University of Technology Thonburi (KMUTT) committed to be Sustainable University for SDGs 2030 since 2017 and committed to sustainability leadership in all of our activities from operations, teaching, to conducting research. Our commitments are to be a sustainable university providing a role model on Energy, Environment, Safety Management Systems and provide sustainability platform to promote sustainability leadership. Sustainable energy management system has been provided by top management policy since 2010 which focus on energy usage reduction, energy efficient appliances usage, renewable energy usage and carbon footprint reduction. To achieve the sustainable energy goal which comply to SDG 7: Affordable and Clean Energy and SDG13: Climate Action, KMUTT focus on student leadership which provide awareness raising, perception, and leading to implementation for all in KMUTT by using SEP for SDG concept. Moreover, KMUTT initiate sustainable strategy which provide the learning environment to make all campus as living lab, promote people participation and monitoring to make continual improvement with positive reinforcement. The sustainable energy goal achieved along with the expansion activities to community surround according to the student engagement. Keyword: energy management, sustainable energy, SEP for SDG, SDG 7, SDG 13

A bottom-up intra-hour proactive scheduling of thermal appliances for household peak avoiding based on model predictive control

Residential-level peak shaving is beneficial for the supply–demand balance of buildings with a limited power capacity of renewable energy sources like building-integrated photovoltaics. Thermostatically controlled loads (TCLs) are recognized as flexible resources for peak shaving. In practice, most thermal appliances are controlled by local on–off controllers without any communication and coordination. Multiple appliances may operate simultaneously and produce load peaks. Existing studies assume the existence of an electricity market and time-varying tariffs, which are not always available. This paper developed a model predictive control (MPC) based bottom-up proactive method to leverage the thermal inertia of residential thermal appliances for avoiding household peak load without relying on time-varying tariffs. First, we used a physics-based modeling approach to model the thermal dynamics of several residential thermal devices. Then, we designed a centralized MPC controller to minimize the appliance concurrent operations considering occupant comfort zones and appliance rate powers. Consequently, the house peak load is reduced by minimizing concurrent operations. Simulations over homogenous and heterogenous appliance set validated that the MPC-based proactive scheduling method can effectively reduce household peak load without compromising user comfort too much. Finally, we discussed the impact of control interval, appliance rated powers, and penalty factors on peak avoiding and the computational feasibility of the MPC controller. We concluded that the MPC-based proactive scheduling is feasible and effective for a limited number of thermal appliances with short horizons (i.e., six-step). Replacing a mixed-integer nonlinear program solver with a mixed-integer quadratic program solver could significantly reduce the computational burden. However, the application of the MPC controller to multiple households at the system level is still impractical. Further research efforts are emphasized, like mining and forecasting occupant demand and appliance usage patterns in households and designing scalable control strategies for system-level peak shaving.

Time series analysis and anomaly detection for trustworthy smart homes

The IoT network is expected to harbor several zettabytes of information in the future. Since trust and integrity are critical to IoT, it is essential to imbibe trust into the IoT environment for ensuring dependability and reliability. We propose a machine learning-based trustworthy system for the IoT-based smart home environment. Multiple appliances connected through the internet are susceptible to privacy issues, hence utmost care must be taken to ensure trust in the network. We consider the energy data and weather information with respect to smart homes, for comprehending the relationship between energy consumption by appliances and time period for detecting anomalous usage of appliances using the SDAR-based Change Finder algorithm. Time series analysis is performed using ARIMA, SARIMA, LSTM, Prophet, Light GBM, and VAR. The evaluation has been performed using RMSE, MSE, and MAE, and the study establishes that the ARIMA model outperforms the other models.

RWD126 A Comparative Product Consumption Analysis Among New Stoma Appliance Users Utilizing Real-World Evidence

A high consumption of stoma appliances and supporting products (SP) can indicate stoma-related challenges with leakage and peristomal skin complications (PSC). The objective of this retrospective analysis was to assess differences in consumption of ostomy bags, plates and SP among new stoma appliance users in United Kingdom (UK) using either Coloplast Sensura Mio® (SM) or SALTS Confidence BE® (CB).