Characterization of the Electrical Consumption Pattern of Household Appliances for Home Energy Management Using High-Resolution Measurement Techniques in IoT Environments

For future smart cities, smart homes will be required. The key elements are the smart use of energy and smart communication systems that are connected to homes. Along with this, the devices inside the house will need to be monitored and managed efficiently. One of the current proposals is the use of Home Energy Management Systems (HEMS) allowing to solve problems associated with efficient management, the economy of electrical energy, and failures/alarms regarding the operation and safety of appliances. This work proposes a model for the recognition of patterns of energy consumption in household appliances, based on the capture of electrical parameters through Smart Socket, using an intrusive method in the electric charge. The data acquisition system corresponds to an IoT platform that uses automatic meter reading elements, which, connected via Wi-Fi, send data to a cloud service. The results obtained allow a characterization of household appliance consumption profiles, with high levels of reliability and under multiple operating states. Because of the foregoing, the detection, monitoring, and control of household appliances connected to the electrical network allow the reduction of both household billing and CO2 emissions

Design and Construction of Voice Controlled Smart Power Strip

This paper focuses on voice control for smart power strips, which constitutes the crux of the study. As technology develops, it is obvious that there is a swift increase in the development of smart homes. This is why the project is important because it combines voice control with a secure biometric activator and a wireless connection between the voice control system and the power strip. Through a combination of programming, in-depth research, simulations and analysis of the results, the proposed technique exhibited better performance than the current techniques. The voice recognition module sends signals to the first transceiver, which acts as the transmitter, and from there, to the second transceiver, which acts as the receiver. Finally, the signal is sent to the relay module, which controls the socke

A comparison of generative and discriminative appliance recognition models for load monitoring

Appliance-level Load Monitoring (ALM) is essential, not only to optimize energy utilization, but also to promote energy awareness amongst consumers through real-time feedback mechanisms. Non-intrusive load monitoring is an attractive method to perform ALM that allows tracking of appliance states within the aggregated power measurements. It makes use of generative and discriminative machine learning models to perform load identification. However, particularly for low-power appliances, these algorithms achieve sub-optimal performance in a real world environment due to ambiguous overlapping of appliance power features. In our work, we report a performance comparison of generative and discriminative Appliance Recognition (AR) models for binary and multi-state appliance operations. Furthermore, it has been shown through experimental evaluations that a significant performance improvement in AR can be achieved if we make use of acoustic information generated as a by-product of appliance activity. We demonstrate that our a discriminative model FF-AR trained using a hybrid feature set which is a catenation of audio and power features improves the multi-state AR accuracy up to 10 %, in comparison to a generative FHMM-AR model

Development of a user-friendly, low-cost home energy monitoring and recording system

This paper reports research undertaken to develop a user-friendly home energy monitoring system which is capable of collecting, processing and displaying detailed usage data. The system allows users to monitor power usage and switch their electronic appliances remotely, using any web enabled device, including computers, phones and tablets. The system aims to raise awareness of consumer energy use by gathering data about usage habits, and displaying this information to support consumers when selecting energy tariffs or new appliances. To achieve these aims, bespoke electrical hardware, or ‘nodes’, have been designed and built to monitor power usage, switch devices on and off, and communicate via a Wi-Fi connection, with bespoke software, the ‘server’. The server hosts a webpage which allows users to see a real-time overview of how power is being used in the home as well as allowing scheduled tasks and triggered tasks (which respond to events) to be programmed. The system takes advantage of well standardised networking specifications, such as Wi-Fi and TCP, allowing access from within the home, or remotely through the internet. The server runs under Debian Linux on a Raspberry Pi computer and is written in Python, HTML and JavaScript. The server includes advanced functionality, such as device recognition which allows users to individually monitor several devices that share a single node. The openPicus Flyport is used to provide Wi-Fi connectivity and programmable logic control to nodes. The Flyport is programmed with code compiled from C

Low-Power Appliance Monitoring Using Factorial Hidden Markov Models

To optimize the energy utilization, intelligent energy management solutions require appliance-specific consumption statistics. One can obtain such information by deploying smart power outlets on every device of interest, however it incurs extra hardware cost and installation complexity. Alternatively, a single sensor can be used to measure total electricity consumption and thereafter disaggregation algorithms can be applied to obtain appliance specific usage information. In such a case, it is quite challenging to discern low-power appliances in the presence of high-power loads. To improve the recognition of low-power appliance states, we propose a solution that makes use of circuit-level power measurements. We examine the use of a specialized variant of Hidden Markov Model (HMM) known as Factorial HMM (FHMM) to recognize appliance specific load patterns from the aggregated power measurements. Further, we demonstrate that feature concatenation can improve the disaggregation performance of the model allowing it to identify device states with an accuracy of 90% for binary and 80% for multi-state appliances. Through experimental evaluations, we show that our solution performs better than the traditional event based approach. In addition, we develop a prototype system that allows real-time monitoring of appliance states

Appliance Recognition in an OSGi-based Home Energy Management Gateway

The rational use and management of energy is considered a key societal and technological challenge. Home energy management systems (HEMS) have been introduced especially in private home domains to support users in managing and controlling energy consuming devices. Recent studies have shown that informing users about their habits with appliances as well as their usage pattern can help to achieve energy reduction in private households. This requires instruments able to monitor energy consumption at fine grain level and provide this information to consumers. While the most existing approaches for load disaggregation and classification require high-frequency monitoring data, in this paper we propose an approach that exploits low-frequency monitoring data gathered by meters (i.e., Smart Plugs) displaced in the home. Moreover, while the most existing works dealing with appliance classification delegate the classification task to a remote central server, we propose a distributed approach where data processing and appliance recognition are performed locally in the Home Gateway. Our approach is based on a distributed load monitoring system made of Smart Plugs attached to devices and connected to a Home Gateway via the ZigBee protocol. The Home Gateway is based on the OSGi platform, collects data from home devices, and hosts both data processing and user interaction logic

A practical review of energy saving technology for ageing populations

Fuel poverty is a critical issue for a globally ageing population. Longer heating/cooling requirements combine with declining incomes to create a problem in need of urgent attention. One solution is to deploy technology to help elderly users feel informed about their energy use, and empowered to take steps to make it more cost effective and efficient. This study subjects a broad cross section of energy monitoring and home automation products to a formal ergonomic analysis. A high level task analysis was used to guide a product walk through, and a toolkit approach was used thereafter to drive out further insights. The findings reveal a number of serious usability issues which prevent these products from successfully accessing an important target demographic and associated energy saving and fuel poverty outcomes. Design principles and examples are distilled from the research to enable practitioners to translate the underlying research into high quality design-engineering solutions

An Implementation of Polyglot Voice Supervise Home Device Using Raspberry Pi

Most of us tend to enjoy the ease of living by doing the bare minimum. The same applies while operating the devices at home by just a few touches or by using our voice in the preferred language. A smart home is an Internet of Things (IoT) platform that uses the internet to control the devices at our home, and this technology has grown enormously over the past few years encouraging new ideas. And with that thought, this system will be implementing Home Automation with Raspberry Pi and Google Assistant by controlling the appliances like lights, fans, air conditioners, temperature sensors, and more, in any preferred language.  Platforms like If This Then That (IFTTT), Adafruit, Message Queueing Telemetry Transport (MQTT), and Raspberry Pi IO are used to connect the hardware with the software that is a common path for devices that are connected to the Relay module and the Google Assistant. The IFTTT platforms are easily available on our smartphones or a website that makes it easy for us to access different devices at different parts of the house or anywhere. Home automation minimizes the manual switching  ON/OFF of the appliances whilst being controlled by the commands that are given by the users. This project builds an automation system that uses the range of Wifi or Bluetooth, which is easily accessible by the users to connect their devices and control them by voice through Google Assistant. This makes it easy for the users to access their devices wherever they are.  Home automation comes as an advantage for older people and especially the physically disabled. The main objective of this proposed project is to provide a comfortable and a digitalized environment to use the day-to-day appliances with added security

Home appliances classification based on multi-feature using ELM

With the development of science and technology, the application in artificial intelligence has been more and more popular, as well as smart home has become a hot topic. And pattern recognition adapting to smart home attracts more attention, while the improvement of the accuracy of recognition is an important and difficult issue of smart home. In this paper, the characteristics of electrical appliances are extracted from the load curve of household appliances, and a fast and efficient home appliance recognition algorithm is proposed based on the advantage of classification of ELM (Extreme Learning Machine). At the same time, the sampling frequency with low rate is mentioned in this paper, which can obtain the required data through intelligent hardware directly, as well as reduce the cost of investment. And the intelligent hardware isdesigned by our team, which is wireless sensor network (WSN) composed by a lot of wireless sensors. Experiments in this paper show that the proposed method can accurately determine theusing electrical appliances. And greatly improve the accuracy of identification, which can further improve the popularity of smart home

A Survey of Devices for Analyzing Electricity Consumption

In this paper, a review of the devices used in collecting, measuring, analyzing and monitoring electric power consumption are presented. These devices are an integral part of embedded systems. The relevant concepts related to challenges in the energy sector are presented, and one method of identifying solution to these challenges is by using a reliable power meter for collecting and monitoring electricity consumption. To obtain recommended devices for data collection and monitoring of electricity consumption, an extensive review of relevant power meters used for data collection is conducted. The information compiled focuses on some of the characteristics of these meters. In carrying out a comprehensive study of these devices, the qualities and benefits of these devices (ease of operation and installation, ability to monitor appliance usage in the building all day, and the ability to reset during power outages) are clearly identified