IoT Based Smart Electrical Meter for Smart Homes

Smart homes integrate several sensors to facilitate information exchange and the execution of tasks. In addition, with the development of the Internet of Things (IoT) platforms, the control of appliances and remote devices has become possible. This sensor collects data in real time to closely monitor the devices of a user’s household. The present study employs a machine learning methodology to perform a global analysis of energy consumption and efficiency in smart homes. In This work we propose two advanced ensemble models to improve the performance of energy consumption in smart homes, the first one is a voting ensemble model based on a ranking weight averaging that combines following basic machine learning techniques: decision tree (DT), random forest (RF), and eXtreme Gradient Boosting (XGB). The second one is the stacking ensemble model in which the basic models (DT-RF-XGB) are combined through stacked generalization, then uses a secondary layer model or meta-learner (RF) to provide output prediction. The findings obtained show that the proposed ensemble model based on DT-RF-XGB using stacking technique surpasses all other basic algorithms with R2 around 0.9825.

In addition to the increase in basic electricity Tariff or Tarif Dasar Listrik (TDL), the high cost of electricity consumption is also influenced by our lack of awareness or lack of care in our daily use of electronic devices. This study aims to design and create a prototype that can monitor and control the use of IoT-based electronic devices or via the internet where electricity consumption data is stored in the Firebase real-time database using the ACS712 current sensor, ZMPT101B voltage sensor, an Arduino microcontroller, and NodeMCU V3. This tool will send current, voltage, and wattage data into the database, and then it will be converted into kWh. Also, the costs arising from electricity consumption based on the PLN electricity base rate, and the results can be seen directly through an Android phone. This system can monitor electricity consumption for four electronic devices. Also, to monitor this system, it can control electronic devices using a relay module, which functions as an electric switch to turn on and off electronic devices controlled via an Android device. This system can monitor electricity usage for three units of electronic equipment, i.e., water pump (unit 1), 14 CRT TV (unit 2), and Dispenser (unit 3). It's also succeeded in reducing electricity consumption. In unit 2, the decline occurred as much as 27.49%, And In unit 3, the fall occurred as much as 57.28%. From the overall test results, the system can be categorized as working well so that it is feasible to be implemented in the household for savings in the use of electricity.

This chapter presents the concept of Smart Home, describes the Smart Home networking technologies and discusses the main issues for ensuring security in a Smart Home environment. Nowadays, the integration of current communication and information technologies within the dwelling has led to the emergence of Smart Homes. These technologies facilitate the building of Smart Home environments in which devices and systems can communicate with each other and can be controlled automatically in order to interact with the household members and improve the quality of their life. However, the nature of Smart Home environment, the fact that it is always connected to the outside world via Internet and the open security back doors derived from the household members raise many security concerns. Finally, by reviewing the existing literature regarding Smart Homes and security issues that exist in Smart Home environments, the authors envisage to provide a base to broaden the research in Smart Home security.

Should we fear the rebound effect in smart homes?

Modeling the electricity consumption by combining land use types and landscape patterns with nighttime light imagery

Smart grid components such as smart home and battery energy management systems, high penetration of renewable energy systems, and demand response activities, require accurate electricity demand forecasts for the successful operation of the electricity distribution networks. For example, in order to optimize residential PV generation and electricity consumption and plan battery charge-discharge regimes by scheduling household appliances, forecasts need to target and be tailored to individual household electricity loads. The recent uptake of smart meters allows easier access to electricity readings at very fine resolutions; hence, it is possible to utilize this source of available data to create forecast models. In this paper, models which predominantly use smart meter data alongside with weather variables, or smart meter based models (SMBM), are implemented to forecast individual household loads. Well-known machine learning models such as artificial neural networks (ANN), support vector machines (SVM) and Least-Square SVM are implemented within the SMBM framework and their performance is compared. The analysed household stock consists of 14 households from the state of New South Wales, Australia, with at least a year worth of 5 min. resolution data. In order for the results to be comparable between different households, our study first investigates household load profiles according to their volatility and reveals the relationship between load standard deviation and forecast performance. The analysis extends previous research by evaluating forecasts over four different data resolution; 5, 15, 30 and 60 min, each resolution analysed for four different horizons; 1, 6, 12 and 24 h ahead. Both, data resolution and forecast horizon, proved to have significant impact on the forecast performance and the obtained results provide important insights for the operation of various smart grid applications. Finally, it is shown that the load profile of some households vary significantly across different days; as a result, providing a single model for the entire period may result in limited performance. By the use of a pre-clustering step, similar daily load profiles are grouped together according to their standard deviation, and instead of applying one SMBM for the entire data-set of a particular household, separate SMBMs are applied to each one of the clusters. This preliminary clustering step increases the complexity of the analysis however it results in significant improvements in forecast performance.

Nowadays, number of energy efficiency measures are being implemented in the smart home environment to conserve the energy consumption. This research attempts to design and develop a solution for an effective energy consumption in a smart home environment. The microcontroller system monitors the electrical energy consumption in real time and to ensure the power consumption is within the limit or predefined maximum demand value. Once the real time power consumption reaches the pre-set value prior to reaching its maximum demand, there will be an alarm triggered to warn the user that the power consumption is about to reach the predefined value. Once the power consumption exceeds the predefined maximum demand, the connected load will be automatically switched off sequentially one by one based on the pre-planned look up table. Results obtained through simulation and practical are presented in this paper. Recommendations are given for further enhancement of the proposed design for the smart home applications.

The Internet of things is a new technological revolution following the computer and Internet. It aims to connect all physical objects existing in the world and forms a network with everything. In recent years, smart home gradually enters into our life. Smart home uses the Internet of things technology to connect all kinds of devices in the home, to achieve a smart home environment. Although the development of smart home has brought a qualitative leap to people’s life, there are many problems in security. Privacy security is one of the challenges to the smart home environment. Attackers can intrude various smart devices in the smart home environment, to achieve the purpose of stealing users’ personal information and privacy. Among these devices, smart cameras are the most intruded frequently. Since many cameras are installed in users’ homes to achieve real-time monitoring of the environment, but the existence of these cameras provides a channel to get information for attackers. In recent years, the leak of video privacy is emerging in an endless stream. According to the researches about privacy protection, this paper proposes a new scheme to selectively encrypt the video captured by the cameras through machine learning technology, so as to protect the personal privacy of users and improve the security of the smart home environment.

Internet of Thing (IoT) is going to make such a world where physical things (smart home appliances, and smart watches etc.) revolutionized the information networks and services providing systems which provide innovative and smart services to human. With smart home technology, our living area is becoming more comfortable and convenient. Smart home technology provides automated, intelligent, smart, innovative and ubiquitous services to residential users through Information Communication Technology (ICT). Due to internet-connected, dynamic and heterogeneous nature of smart home environment creates new security, authentication and privacy challenges. In this paper, we investigate security attacks in smart home and evaluate their impact on the overall system security. We identified security requirements and solutions in the smart home environment. Based on several scenarios, we suggest to set security goals for the smart home environment. Based on historical data, we forecast security attacks (like malware, virus etc.) and estimated that how many attacks are expected to be launched in coming years.

In recent years, the problem of electrical load forecasting gained attention due to the arrival of new measurement technologies that produce electrical energy consumption data at very short intervals of time. Such short term measurements become voluminous in very short time. The availability of big electrical consumption data allows machine learning techniques to be employed to analyze consumption behavior of every consumer on a greater detail. Predicting the consumption of a residential customer is crucial at this point of time because tailor-made consumer-specific tariffs will play a vital role in load balancing process of Utilities. This paper analyzes the electrical consumption of a single residential customer measured using a smart meter that is capable of measuring electrical consumption at circuit level. The issues and challenges in collecting the data and pre-processing required for making them suitable for data analytics are discussed in detail. A comparison of the performance of different machine learning algorithms implemented using Python’s Scikit-learn module gives an insight on the consumption pattern.

Smart homes are an emerging paradigm of Internet of Things (IoT) in which users can remotely control various home devices via the internet anytime and anywhere. However, smart home environments are vulnerable to security attacks because an attacker can inject, insert, intercept, delete, and modify transmitted messages over an insecure channel. Thus, secure and lightweight authentication protocols are essential to ensure useful services in smart home environments. In 2021, Kaur and Kumar presented a two-factor based user authentication protocol for smart homes using elliptic curve cryptosystems (ECC). Unfortunately, we demonstrate that their scheme cannot resist security attacks such as impersonation and session key disclosure attacks, and also ensure secure user authentication. Moreover, their scheme is not suitable in smart home environments because it utilizes public-key cryptosystems such as ECC. Hence, we design a secure and lightweight three-factor based privacy-preserving authentication scheme for IoT-enabled smart home environments to overcome the security problems of Kaur and Kumar’s protocol. We prove the security of the proposed scheme by using informal and formal security analyses such as the ROR model and AVISPA simulation. In addition, we compare the performance and security features between the proposed scheme and related schemes. The proposed scheme better provides security and efficiency compared with the previous schemes and is more suitable than previous schemes for IoT-enabled smart home environments.

Over the last fifty years, societies across the world have experienced multiple periods of energy insufficiency with the most recent one being the 2022 global energy crisis. In addition, the electric power industry has been experiencing a steady increase in electricity consumption since the second industrial revolution because of the widespread usage of electrical appliances and devices. Newer devices are equipped with sensors and actuators, they can collect a large amount of data that could help in power management. However, current energy management approaches are mostly applied to limited types of devices in specific domains and are difficult to implement in other scenarios. They fail when it comes to their level of autonomy, flexibility, and genericity. To address these shortcomings, we present, in this paper, an automated energy management approach for connected environments based on generating power estimation models, representing a formal description of energy-related knowledge, and using reinforcement learning (RL) techniques to accomplish energy-efficient actions. The architecture of this approach is based on three main components: power estimation models, knowledge base, and intelligence module. Furthermore, we develop algorithms that exploit knowledge from both the power estimator and the ontology, to generate the corresponding RL agent and environment. We also present different reward functions based on user preferences and power consumption. We illustrate our proposal in the smart home domain. An implementation of the approach is developed and two validation experiments are conducted. Both case studies are deployed in the context of smart homes: (a) a living room with a variety of devices and (b) a smart home with a heating system. The obtained results show that our approach performs well given the low convergence period, the high level of user preferences satisfaction, and the significant decrease in energy consumption.

Smart Home research started in 2000. Since then, Smart Home has attracted the attention of many researchers, and several studies have been conducted. This study aims to chart Smart Home research trends using bibliometric analyses from all years up to the publication date indexed in Scopus. We use keywords related to Smart Home in the Scopus database. Analysis techniques that use parameters include the year of publication, Scopus quartile range, year of publication, type of publication, journal publication, the publication of the most frequently cited maps, and density display mapping. The results show that the Q1 journal dominated since the first Smart Home publication in 2000 and peaked in 2013. The journal article type is the most commonly published compared to research conferences and other publications. Journal publications show the majority of IEEE transactions in consumer electronics. Three Smart Home publications have been cited more than 1563 times. This study also found that the words ‘smart home’, ‘technology,’ and ‘management’ are widely used by researchers. For future research, you should use keywords like ‘smart home energy management,’ ‘smart home environment,’ ‘sensor,’ ‘electric vehicle,’ and ‘smart home technology.’

Electricity theft is the illegal use of electrical power without proper authorization and payment. This act causes losses to the electricity provider and the public, poses safety hazards, and violates the law. Perusahaan Umum Daerah (PERUMDA) Tuah Sekata of Pelalawan Regency, as a regional-owned enterprise responsible for electricity distribution to the community, annually detects indications of electricity theft through field inspections. To simplify the identification process and reduce operational costs, an Internet of Things (IoT)-based device was designed to monitor and control customers’ electricity consumption. The device utilizes a power supply as a 5V DC voltage source, a PZEM-004T sensor to measure voltage, current, power, and energy, an LCD 16x2 to display measurement results, and a 5V relay to connect or disconnect the customer’s power supply. An ESP32 microcontroller processes the data and transmits it via Wi-Fi to the Blynk application, which serves as an online monitoring interface. The device developed in this study not only facilitates real-time monitoring of electricity consumption but also serves to detect and prevent potential electricity theft. The experimental results show that, for voltage measurements, the average percentage error obtained was 0.41% (Device IoT 1), 0.29% (Device IoT 2), and 0.38% (Device IoT 3). Meanwhile, for current measurements, the average error rates were 8.42% (Device IoT 1), 8.66% (Device IoT 2), and 8.60% (Device IoT 3). Based on the experimental results, it can be concluded that the three developed IoT devices have an error rate of less than 10% (classified as very good), making them feasible for use in monitoring electrical power consumption.

A multi-level multi-objective strategy for eco-environmental management of electricity market among micro-grids under high penetration of smart homes, plug-in electric vehicles and energy storage devices