Appliance Control Research Articles

An efficient hybrid model for appliances classification based on time series features

Today, depending on the increasing population and technological developments, household appliances have significantly increased. This situation causes an ever-increasing energy demand, especially in modern societies. However, efficient energy use is also critical due to limited energy resources. With the Non-intrusive load monitoring methods, the usage of home appliances can be controlled, and behaviours can be adjusted for the energy saving of users. The control of home appliances is only possible with the effective detection of these appliances. In this study, a new hybrid model including a sliding window approach, long short-term memory (LSTM) network, and classifier is proposed to detect house appliances. Unlike traditional feature extraction, the proposed model uses time-series features to determine device features. For this purpose, each time series is divided into windows that do not overlap with the sliding window approach. The mean, standard deviation, median, and multiscale entropy values are calculated from each window. These values are combined and applied to the LSTM network for deep feature extraction. It is applied to k nearest neighbor (k-NN), Ensemble Learning (EBT), and support vector machine (SVM) classifiers to detect residential appliances from deep features retrieved from the LSTM network. The proposed model's effectiveness has been tested with the high-resolution profiles of the residential appliances dataset. In experimental studies, residential appliances were detected in LSTM based k-NN, EBT, and SVM classifiers with an accuracy of 98.25%, 97.81%, and 97.38%, respectively.

Performance of a Mid-Size Net-Zero Energy Solar House

The University of Alabama at Birmingham (UAB) was one of 16 collegiate teams from around the world that participated in the U.S. Department of Energy Solar Decathlon 2017 competition. An interdisciplinary team of students from across the university was engaged in a 2-year long process to design and build a house that is powered completely by solar power. The house was equipped to run all the typical appliances of an average modern house at similar levels on a conventional power grid. The net-zero house was built and tested on the UAB campus. Considering Birmingham’s weather, a safe room was built to ensure the safety of occupants during events of extreme weather, such as a tornado. A ductless HVAC unit consisting of an inverter-type 3-speed outdoor unit supplied refrigerant to four high-wall indoor units providing the primary source of space conditioning. To achieve a model of efficiency and cost effectiveness, the house incorporated a heavily insulated envelope and precise glazing protection. The roof, floor framing and walls had R-30 batt and foam insulation. With a design informed by southern vernacular language, the building is oriented to maximize solar access and to use roof planes for shading the majority of the year. Peak power generation of the panels was recorded at 9.6 kW. The home has a centralized energy management system that can provide access to energy consumption data and allow control of lighting, appliances, and plug loads remotely. Energy modeling showed that the annual electricity consumption for heating and cooling with variation in wall types were 8470 to 11,661 kWh. For the month of October, it was calculated varying from 683 to 763 kWh, with varying air changes per hour from 0 to 1.5.

Analysis of supervised graph signal processing-based load disaggregation for residential demand-side management

With the widespread adoption of advanced metering infrastructure (AMI) smart meters in recent years, interest in using residential buildings as demand-side management resources has increased. To characterize the amount of energy available for demand-side management in residential buildings, non-intrusive load monitoring (NILM) may be applied to the low-resolution data collected by AMI smart meters to provide insight into the energy consumption of individual controllable appliance types within the house. The disaggregation of water heaters and HVAC system components is investigated in this study, as these appliances are the greatest consumers of energy in U.S. homes. A supervised graph signal processing (GSP) algorithm is applied to real power data for appliances sampled at a 15-minute rate, with results demonstrating that disaggregation sequencing, graph scaling factor, and graph classifier threshold values have a significant impact on supervised GSP NILM accuracy. Scaling factor (ρ) was varied from 20 to 200, graph signal classifier threshold value (q) was varied from 0 to 0.5, and six disaggregation sequences were considered. Varying these parameters resulted in a maximum range in accuracy, as measured by F-measure, of 0.0 to 1.0 for a single appliance, demonstrating that choosing a set of optimal parameters is critical for disaggregation accuracy.

Design and Development of an Assistive System Based on Eye Tracking

This research concerns the design and development of an assistive system based on eye tracking, which can be used to improve the quality of life of disabled patients. With the use of their eye movement, whose function is not affected by their illness, patients are capable of communicating with and sending notifications to caretakers, controlling various appliances, including wheelchairs. The designed system is divided into two subsystems: stationary and mobile assistive systems. Both systems provide a graphic user interface (GUI) that is used to link the eye tracker with the appliance control. There are six GUI pages for the stationary assistive system and seven for the mobile assistive system. GUI pages for the stationary assistive system include the home page, smart appliance page, eye-controlled television page, eye-controlled air conditional page, i-speak page and entertainment page. GUI pages for the mobile assistive system are similar to the GUI pages for the stationary assistive system, with the additional eye-controlled wheelchair page. To provide hand-free secure access, an authentication based on facial landmarks is developed. The operational test of the proposed assistive system provides successful and promising results.

Dealing with the stochastic prosumager problem with controllable loads

AbstractThis paper focuses on the home energy management for a residential prosumager with flexible loads. In particular, three different types of controllable appliances (shiftable, interruptible, thermostatically controllable) have been considered, each one with a specific representation of energy consumption profile and a potential discomfort rate for the user. The inherent uncertainty affecting the main model parameters (i.e., non- controllable loads, solar production, external temperature) is explicitly accounted for by adopting the two-stage stochastic programming modeling paradigm. The model solution provides the prosumager with the optimal scheduling of the controllable loads and the operation of the storage system that guarantee the minimum expected energy procurement cost, taking into account the overall discomfort. A preliminary computational experience has shown the effectiveness of the proposed approach in terms of cost savings and the advantage related to the use of a stochastic programming approach over a deterministic formulation.

Multiobjective Metaheuristic Load Control Algorithm for Interaction between Smart Home and Humans

Technology has boosted electric power consumption both locally and worldwide, resulting in a substantial increase in demand for electric power. A multiobjective smart house human-computer interaction load control method is suggested to meet the goal of lowering power usage and pricing in smart home load control. It presents a model that includes marginal costs and establishes an electricity price model that takes the load rate into account. It identifies switch appliances and temperature control appliances and gathers human activities, indoor and outdoor temperature, and light intensity using intelligent equipment to create a multiparameter comfort model. It creates a multiobjective model of comfort and electricity price with the purpose of reducing electricity price and multiparameter comfort, and it improves particle swarm performance by using a distance ratio based on fitness value. The optimization algorithm solves the model, determines the best smart home human-computer interaction load control scheme, creates the smart home remote control system’s functional modules, and optimizes the multiobjective smart home human-computer interaction load control algorithm using a frequency-duration parameter tracking learning model. The testing findings suggest that the proposed algorithm can cut power prices in a reasonable manner, as well as regulating 40% reduced electricity usage and load in smart homes.

PERANCANGAN INTERNET OF THINGS (IOT) SMART HOME

The main purpose of this research is to design a smart home using IoT where the technology is to automate various activities at home such as turning off and turning on the lights, television, air conditioner, fan sound system and opening the gate. All devices are connected to the internet via a router, to allow remote monitoring and control of various household appliances. In this paper, I implemented a smart home using the newly released cisco tracer package simulation software, because different IoT devices used for home automation are included in this new version of the simulator. The previous software only had network devices, but in the newly released IoT simulator devices are included sensors, boards, IoT devices. To smartly design the house I use different devices that are used for home security, safety and well-being of the home environment.
 
 
 Keywords: IoT, Smart home, packer tracer, sensor.

Home Automation Using Augmented Reality (HAAR)

The development of Smart Home Controllers has seen rapid growth in recent years, especially for smart devices, that can utilize the Internet of Things (IoT). However, a large portion of the household devices and appliances already in use, are not IoT enabled, and therefore, requires their default control mechanisms for the devices to operate. This paper proposes a smart appliance controller that uses Augmented Reality, MQTT, and other up-to-date platforms to control aftermarket home appliances in the most efficient manner. The proposed work integrates mobile AR with IoT, to control household appliances, with the help of infrared (IR) signals. The characteristics of the system are evaluated through a series of tests and performance measures. The results of the test highlight the quick response time of MQTT for the implementation of a Home Automation System, when compared to the request–reply protocol: CoAP (4 times as fast).

AI-Based Home Energy Management System Considering Energy Efficiency and Resident Satisfaction

Demand-side energy management is becoming increasingly important owing to concerns related to global warming and energy shortages. In particular, as the development of Internet of Things (IoT) enables the precise control of home appliances, the demand for home energy management systems (HEMSs) is expected to increase. This article proposes an artificial intelligence-based HEMS (AI-HEMS) that provides both energy efficiency and resident satisfaction. To this end, we implemented three prediction mechanisms: 1) derivation of comfort temperature; 2) device-free sleep prediction; and 3) occupancy-probability-based outing prediction. Based on these mechanisms, we present four intelligent heater control strategies: 1) outing; 2) occupancy; 3) comfort; and 4) sleep-based control. For evaluation, an experimental testbed was constructed and measurements were taken. To increase the reliability of the evaluation by excluding natural energy fluctuation factors, a treatment group (100 households) and a control group (2281 households) were recruited to measure the energy savings of the treatment group compared to the control group. In a 48-day evaluation, AI-HEMS was found to provide an energy-saving rate of approximately 14% and resident satisfaction of approximately 91%. These results imply that the proposed system can save energy while maintaining a high level of satisfaction.

Enhanced Intelligent Smart Home Control and Security System Based on Deep Learning Model

Security of lives and properties is highly important for enhanced quality living. Smart home automation and its application have received much progress towards convenience, comfort, safety, and home security. With the advances in technology and the Internet of Things (IoT), the home environment has witnessed an improved remote control of appliances, monitoring, and home security over the internet. Several home automation systems have been developed to monitor movements in the home and report to the user. Existing home automation systems detect motion and have surveillance for home security. However, the logical aspect of averting unnecessary or fake notifications is still a major area of challenge. Intelligent response and monitoring make smart home automation efficient. This work presents an intelligent home automation system for controlling home appliances, monitoring environmental factors, and detecting movement in the home and its surroundings. A deep learning model is proposed for motion recognition and classification based on the detected movement patterns. Using a deep learning model, an algorithm is developed to enhance the smart home automation system for intruder detection and forestall the occurrence of false alarms. A human detected by the surveillance camera is classified as an intruder or home occupant based on his walking pattern. The proposed method’s prototype was implemented using an ESP32 camera for surveillance, a PIR motion sensor, an ESP8266 development board, a 5 V four-channel relay module, and a DHT11 temperature and humidity sensor. The environmental conditions measured were evaluated using a mathematical model for the response time to effectively show the accuracy of the DHT sensor for weather monitoring and future prediction. An experimental analysis of human motion patterns was performed using the CNN model to evaluate the classification for the detection of humans. The CNN classification model gave an accuracy of 99.8%.

Hierarchical Multiobjective Distributed Deep Learning for Residential Short-Term Electric Load Forecasting

Short-term load forecasting plays an essential role in appliance control in households and demand response at the neighborhood or community level. When load forecasting is simultaneously performed at different levels in a smart community, a central method that directly aggregates raw household data may incur a security issue. However, an individual method that builds separate models for each level requires additional data collection. We propose a distributed load forecasting framework that is secure, data efficient, and resource efficient. Our proposed method shares the latent variables, the reduced expression of the input data, of the household models without revealing the raw data. The latent variables are aggregated and fed to the neighborhood model, and during inference, input data collection can be abbreviated in the neighborhood. Because household models provide informative latent variables, the number of model parameters for the neighborhood model can be reduced. Our proposed method is evaluated using real electricity consumption data for six different models under 24 h and 1 h ahead forecasting. Our proposed method with the long short-term memory, multi-layer perceptron, and convolution neural network-based models presents the degradation in average mean absolute percentage error 3.68% and 5.16% at most for household and neighborhood forecasting, respectively, compared to the individual method. At the same time, it reduces the neighborhood’s model size by approximately 53%.

Investigation of Al & Cu for control, operation and maintenance of starter

The condition monitoring of electrical appliances is categorized as a crucial process. The current research deals with efficient control of electrical appliances, which are far away from the operator and condition monitoring of the connected electrical appliances. The starting of the motor through starter will be controlled using the electrical setup, operated through the Wi-Fi connected to the network through GSM module. As Aluminium & Copper are used in domestic motors and appliances, we explored the properties of these metals for the ease of maintenance, conductivity, reliability and to reduce the cost. The ultimate goal of this research is to carry out controlling and monitoring efficiently and to provide ready to use setup for the mankind. Copper tends to have higher conductivity & consistency compared to aluminium. Copper is the suitable and most appropriate material to be used in three-phase induction motor starter. The required speed is achieved in one minute using Blynk application.This methodology is one of the bests to control and monitor the electrical device with ease. This methodology can reduce many complications in the current day-to-day life. This also overcomes the criticalities such as manual monitoring and longer duration for troubleshooting.

An IoT-Based Home Automation and Weather Monitoring System

This paper reports an IoT-based home automation and weather monitoring system developed for remote control of household appliances and weather monitoring. The proposed system comprises a Wi-Fi enabled ESP32 microcontroller as the network gateway, a relay module, and a DHT11 sensor for monitoring temperature and humidity. The status of the household devices and the weather data are continuously collected and stored in a MySQL database, which is set up on the IoT platform. A user interface was created using two different protocols – HyperText Transfer Protocol (HTTP) and Message Queuing Telemetry Transport (MQTT). The IoT-based home automation and weather monitoring system is designed to be low cost, user-friendly and can remotely control home appliances and monitor weather data over the internet.

A Novel Online Bci System Using Speech Imagery and Ear-Eeg for Home Appliances Control

A Novel Online Bci System Using Speech Imagery and Ear-Eeg for Home Appliances Control

DTMF-based load control system

The aim of this paper is to create a remote-controlled home automation system using a mobile phone. One of the newest trends in home appliance control is home automation. Traditional wall switches are located throughout the home and must be physically pressed to turn ON or OFF loads. It is extremely difficult for the elderly or disabled. This technology is designed to enable mobile phone users to control home appliances by dialing a specific number. The advantage of the Dual-Tone Multi-Frequency (DTMF) system is that it can be controlled remotely from a distance, which reduces the burden on a person. Two mobile phones make up a DTMF based system, one phone serves as a remote control and the other as a receiver. Unlike conventional infrared and Radio Frequency (RF) controllers, it may be used to switch devices from anywhere. The device has an MT8870 DTMF decoder, which decodes DTMF tones. This proposed system gives a new direction to the development of home automation.

Hand Pose Recognition Using Parallel Multi Stream CNN.

Recently, several computer applications provided operating mode through pointing fingers, waving hands, and with body movement instead of a mouse, keyboard, audio, or touch input such as sign language recognition, robot control, games, appliances control, and smart surveillance. With the increase of hand-pose-based applications, new challenges in this domain have also emerged. Support vector machines and neural networks have been extensively used in this domain using conventional RGB data, which are not very effective for adequate performance. Recently, depth data have become popular due to better understating of posture attributes. In this study, a multiple parallel stream 2D CNN (two-dimensional convolution neural network) model is proposed to recognize the hand postures. The proposed model comprises multiple steps and layers to detect hand poses from image maps obtained from depth data. The hyper parameters of the proposed model are tuned through experimental analysis. Three publicly available benchmark datasets: Kaggle, First Person, and Dexter, are used independently to train and test the proposed approach. The accuracy of the proposed method is 99.99%, 99.48%, and 98% using the Kaggle hand posture dataset, First Person hand posture dataset, and Dexter dataset, respectively. Further, the results obtained for F1 and AUC scores are also near-optimal. Comparative analysis with state-of-the-art shows that the proposed model outperforms the previous methods.

Intelligent Scheduling of Smart Home Appliances Based on Demand Response Considering the Cost and Peak-to-Average Ratio in Residential Homes

With recent developments, smart grids assured for residential customers the opportunity to schedule smart home appliances’ operation times to simultaneously reduce both the electricity bill and the PAR based on demand response, as well as increasing user comfort. It is clear that the multi-objective combinatorial optimization problem involves constraints and the consumer’s preferences, and the solution to the problem is a difficult task. There have been a limited number of investigations carried out so far to solve the indicated problems using metaheuristic techniques like particle swarm optimization, mixed-integer linear programming, and the grey wolf and crow search optimization algorithms, etc. Due to the on/off control of smart home appliances, binary-coded genetic algorithms seem to be a well-fitted approach to obtain an optimal solution. It can be said that the novelty of this work is to represent the on/off state of the smart home appliance with a binary string which undergoes crossover and mutation operations during the genetic process. Because special binary numbers represent interruptible and uninterruptible smart home appliances, new types of crossover and mutation were developed to find the most convenient solutions to the problem. Although there are a few works which were carried out using the genetic algorithms, the proposed approach is rather distinct from those employed in their work. The designed genetic software runs at least ten times, and the most fitting result is taken as the optimal solution to the indicated problem; in order to ensure the optimal result, the fitness against the generation is plotted in each run, whether it is converged or not. The simulation results are significantly encouraging and meaningful to residential customers and utilities for the achievement of the goal, and they are feasible for a wide-range applications of home energy management systems.

Appliance Control with IOT-Arduino of Voice Command Detection for Mobility Impaired People

People with physical mobility disabilities experience limited ability to perform daily routine. Integration between home appliances and Internet of Thing (IoT) can be used to support these people. The technology allows tasks to be performed much more easily and enable access to applications and services that would be a challenge with existing digital user interfaces. However, the developed system that is commercially available is costly and equipped with highly specific interfaces which may require considerable technical competency to conduct system interfacing. The purpose of this study is to develop a low cost IOT system for voice-enabled lamp control by utilizing Google Assistant that is available in commercial Android based mobile devices, allowing user to operate lighting appliances without moving. Personal assistance using Google platform embedded with Arduino microcontroller, ESP8266-based NodeMCU development board and integrated Blynk application allow for remote monitoring capability. The system is remotely controlled from a tablet or smartphone to assist disabled people with restricted mobility, in particular those with lower limb disabilities. The system had been tested and demonstrated that the lamp control can be easily accessed by voice command that is then validated using voice pitch analyzer. Test result indicated that capability of the integrated voice control system is verified up to a maximum distance of 24.5 meter, making it a potential implementation in any customized smart home or room system for people with mobile disability.

Design Multitasking Device to Protect and Control of Electrical Home Appliances

Design Multitasking Device to Protect and Control of Electrical Home Appliances

Artificial Intelligence Algorithms in Visual Evoked Potential-Based Brain-Computer Interfaces for Motor Rehabilitation Applications: Systematic Review and Future Directions.

Brain-Computer Interface (BCI) is a technology that uses electroencephalographic (EEG) signals to control external devices, such as Functional Electrical Stimulation (FES). Visual BCI paradigms based on P300 and Steady State Visually Evoked potentials (SSVEP) have shown high potential for clinical purposes. Numerous studies have been published on P300- and SSVEP-based non-invasive BCIs, but many of them present two shortcomings: (1) they are not aimed for motor rehabilitation applications, and (2) they do not report in detail the artificial intelligence (AI) methods used for classification, or their performance metrics. To address this gap, in this paper the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology was applied to prepare a systematic literature review (SLR). Papers older than 10 years, repeated or not related to a motor rehabilitation application, were excluded. Of all the studies, 51.02% referred to theoretical analysis of classification algorithms. Of the remaining, 28.48% were for spelling, 12.73% for diverse applications (control of wheelchair or home appliances), and only 7.77% were focused on motor rehabilitation. After the inclusion and exclusion criteria were applied and quality screening was performed, 34 articles were selected. Of them, 26.47% used the P300 and 55.8% the SSVEP signal. Five applications categories were established: Rehabilitation Systems (17.64%), Virtual Reality environments (23.52%), FES (17.64%), Orthosis (29.41%), and Prosthesis (11.76%). Of all the works, only four performed tests with patients. The most reported machine learning (ML) algorithms used for classification were linear discriminant analysis (LDA) (48.64%) and support vector machine (16.21%), while only one study used a deep learning algorithm: a Convolutional Neural Network (CNN). The reported accuracy ranged from 38.02 to 100%, and the Information Transfer Rate from 1.55 to 49.25 bits per minute. While LDA is still the most used AI algorithm, CNN has shown promising results, but due to their high technical implementation requirements, many researchers do not justify its implementation as worthwile. To achieve quick and accurate online BCIs for motor rehabilitation applications, future works on SSVEP-, P300-based and hybrid BCIs should focus on optimizing the visual stimulation module and the training stage of ML and DL algorithms.