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The massive dissemination of smart devices in current markets provides innovative technologies that can be used in energy management systems. Particularly, smart plugs enable efficient remote monitoring and control capabilities of electrical resources at a low cost. However, smart plugs, besides their enabling capabilities, are not able to acquire...
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... algorithm forecasts, with a 91% accuracy, if the door of the refrigerator will be open during the next hour-meaning that at least one user will be at the kitchen in the next hour. Table 3 shows the detailed results for the Configuration 1.1.2 with 20 nodes in the hidden layer and using a 3496 epochs training. ...
Citations
... Numerous sensors, including accelerometers, magnetometers, noise sensors, electromyography, gyroscopes, and radar, can be positioned on people, on things, or in the environment. As a result, there are three distinct categories into which the sensor-based solutions can be subdivided: wearable [6], sensor on objects [7], and ambient sensor [8]. [17], which signifies HAR applications that combine various types of sensors, like combining ambient and object sensors that can record both the object movements and environment state. ...
Human activity recognition (HAR) is a technology that infers current user activities by using the available sensory data network. Research on activity recognition is considered extremely important, particularly when it comes to delivering sensitive services such as healthcare services and live tracking assistance and autonomy. For this purpose, many researchers have proposed a knowledge-driven approach or data-driven reasoning for identification techniques. However, there are multiple limitations associated with these approaches and the resulting models are typically not complete enough to capture all types of human activities. Thus, recent works have suggested combining these techniques through a hybrid model. This paper's goal is to give a brief overview of activity recognition implementation approaches by looking at various sensing technologies used to gather data from internet of things (IoT) gadgets, looking at preprocessing and feature extraction approaches, and then comparing methods used to identify human activities in smart homes, and highlighting their strengths and weaknesses across various fields. Numerous pertinent works were located, and their accomplishments were assessed.
... Smart functionality can also be found in ovens [60] to monitor and control the cooking process or refrigerators [61] to record the stored goods and help in scheduling the resident's shopping routine. Smart plugs [62] form another device category that is gaining interest. These plugs are connected to the home's electricity grid, and ordinary devices are connected to the smart plugs. ...
... Smart plugs, which are used to control various electronic devices remotely, amplify the security challenges in a smart home [62]. These devices can switch appliances on or off and monitor energy usage, providing convenience and efficiency. ...
As the Internet of Things (IoT) continues to revolutionize the way we interact with our living spaces, the concept of smart homes has become increasingly prevalent. However, along with the convenience and connectivity offered by IoT-enabled devices in smart homes comes a range of security challenges. This paper explores the landscape of smart-home security. In contrast to similar surveys, this study also examines the particularities of popular categories of smart devices, like home assistants, TVs, AR/VR, locks, sensors, etc. It examines various security threats and vulnerabilities inherent in smart-home ecosystems, including unauthorized access, data breaches, and device tampering. Additionally, the paper discusses existing security mechanisms and protocols designed to mitigate these risks, such as encryption, authentication, and intrusion-detection systems. Furthermore, it highlights the importance of user awareness and education in maintaining the security of smart-home environments. Finally, the paper proposes future research directions and recommendations for enhancing smart-home security with IoT, including the development of robust security best practices and standards, improved device authentication methods, and more effective intrusion-detection techniques. By addressing these challenges, the potential of IoT-enabled smart homes to enhance convenience and efficiency while ensuring privacy, security, and cyber-resilience can be realized.
... The first version of EnaPlug was proposed in 2017, with application in a refrigerator, according to reference [4], containing an Arduino Mega, an Ethernet Shield, a MAX 485, a power analyzer (CVM-1D), a door opening sensor, external and internal temperature sensors, an internal humidity sensor, and a relay. References [4][5][6][7] highlight the use of the Environmental Awareness smart Plug (EnAPlug) as a solution for application in a research and study centers to enable the study, testing, and validation of methodologies and models for energy management inside buildings. In [6], a new update is proposed that allows access to the operating system, enabling the processing and storage of data with a focus on the possibilities of learning in the consumption profiles and habits of users, in addition to the sharing of information between the different EnAPlugs. ...
... EnAPlug's proposal consists of integrating several sensors to better understand the environment where the specific load is located and not just limited to energy consumption, operating status, activation, and control [6]. EnAPlug, compared to Smart plugs available on the market, stands out for exploring information about the environment in which the device is inserted, such as temperature, humidity, luminosity and other important parameters that can be entered through new sensors, i.e., data that can directly or indirectly affect the results of energy management, in addition, it allows access to data locally, differentiating it from others that are done centrally and in the cloud, in addition to enabling the application of machine learning in embedded systems and edge, through Tiny Machine Learning (TinyML) [5,6]. ...
... EnAPlug's proposal consists of integrating several sensors to better understand the environment where the specific load is located and not just limited to energy consumption, operating status, activation, and control [6]. EnAPlug, compared to Smart plugs available on the market, stands out for exploring information about the environment in which the device is inserted, such as temperature, humidity, luminosity and other important parameters that can be entered through new sensors, i.e., data that can directly or indirectly affect the results of energy management, in addition, it allows access to data locally, differentiating it from others that are done centrally and in the cloud, in addition to enabling the application of machine learning in embedded systems and edge, through Tiny Machine Learning (TinyML) [5,6]. ...
Given the growth of domotics and home automation, there is a need to use smart devices that integrate energy management systems and enable the automation of the environment. Considering the need to study the relationship between the environmental parameters in which the equipment is located and the energy parameters, an Environmental Awareness smart Plug (EnAPlug) is proposed with the application of machine learning (Tiny ML).This article presents a demonstration of EnAPlug applied to a refrigerator for predictions on internal humidity and activation motor for 5 min-ahead prediction on its operation, i.e., turning on or off. The two models for forecasting humidity presented Root Mean Squared Error (RMSE) results of 0.055 and 0.058 and a Coefficient of determination (r2 score) of 0.97 and 0.99, respectively. For the motor activation prediction, the results obtained were an accuracy of 94.74% and 94.84%, an F1 score of 0.97 for OFF, 0.94 for ON for Forecast 1 and 0.97 for OFF and 0.93 for ON for Forecast 2. Although the prototype does not have commercial purposes, what differs from existing smart plugs is the option to store data locally. The results are promising, as it allows for better energy management with implementation of machine learning.
... offered by the study in [21]. Intelligent outlets, which enable developers and managers of buildings to track, observe, and manage connect devices using whether independent cloud-based providers or particular building control systems, have largely supplanted previous ways of household gadget surveillance, despite being unusual. ...
... or cloud-based data analytics platforms. Existing procedures immediately revealed sensitive resident data without protecting privacy [21]. ...
In order to enhance a building's functionality and energy efficiency, smart construction technology combines controlled reliable controllers and programs with connected sensors, smart energy products, and data analytics software to track ambient data and resident energy usage patterns. The integration of intelligent technologies and controllers in the construction sector is steadily expanding as a result of their growing significance in the fabrication, business-related; it and scholarly domains. This investigation uses a systematic strategy to review the related published literature between 2013 and 2023.Prior to investigating the rating of publications based on quality evaluation and yearly trend publication was done. The study further classifies the literature based on integration technologies and application. The finding reveals that most of the literature features with occupants an interface to schedule, monitor, and adjust energy consumption profiles. These developments also enable utilities to engage in an exchange with the grid by means of demand response strategies and automatic feedback interruption features.Potential study directions are also examined in the paper, particularly with regard to seamless integration, privacy, and security enhancements. It is also proposed that continuous surveillance of data and machine learning are two ways to enhance the intelligence of smart building technology.
... Sensor-based HAR systems comprise on-body or wearable sensing [49], sensors placed on objects [25,50], and ambient or in-the-environment sensors [51]. HAR methods comprise various sensors that are networked and connected with numerous devices to track the resident's activity or behavior. ...
With the growing interest in smart home environments and in providing seamless interactions with various smart devices, robust and reliable human activity recognition (HAR) systems are becoming essential. Such systems provide automated assistance to residents or to longitudinally monitor their daily activities for health and well-being assessments, as well as for tracking (long-term) behavior changes. These systems thus contribute towards an understanding of the health and continued well-being of residents. Smart homes are personalized settings where residents engage in everyday activities in their very own idiosyncratic ways. In order to provide a fully functional HAR system that requires minimal supervision, we provide a systematic analysis and a technical definition of the lifespan of activity recognition systems for smart homes. Such a designed lifespan provides for the different phases of building the HAR system, where these different phases are motivated by an application scenario that is typically observed in the home setting. Through the aforementioned phases, we detail the technical solutions that are required to be developed for each phase such that it becomes possible to derive and continuously improve the HAR system through data-driven procedures. The detailed lifespan can be used as a framework for the design of state-of-the-art procedures corresponding to the different phases.
... Saudi Arabia has intensified its efforts in this area. The country's 2030 vision aims to reduce energy consumption across various sectors by 20%, potentially saving about one million barrels of oil per day [5] [6]. Traditional meters and smart meters are the two main types of current electricity monitoring solutions. ...
In this paper a system to monitor and control the electricity consumption by means of Internet of Things (IoT) technology in buildings is presented. Wi-Fi smart plugs act as sensors to provide real-time power consumption data on a per device basis. This information is passed to a cloud-based platform via MQTT for further analysis. This mobile app is used to access the building's energy usage data in both real-time and historical, as well allowing control over connected devices from anywhere. The architecture of the system, i.e. hardware components, software infrastructure as well data flow are explained in details. The results show that the IoT-based smart plugs are providing accurate power consumption data with a low deviation. By providing electricity consumers with a greater level of detail on how and when they consume their power, this system can enable them to consciously exercise control over their energy consumption; which may result in reductions in cost as well as levels of inefficiency from the amount used.
... There are many available devices on the market that provide the ability to manage and monitor nonintelligent loads, such as Fibaro, Sonoff, Pacific Sun, Ocean ABB or Xiaomi MI Smart plugs. There are also many works showing the use of these smart plugs in energy management systems [9][10][11]. Smart plugs enable the retrofitting of electrical resources and provide basic functions, such as planning or creating rules. ...
... In Tables 9 and 10, in bold, are receivers whose activation time depends only on the human, causing stochastic changes in the shape of the electricity demand profile. One can see that in residential buildings (buildings [11][12][13][14] at least 50% are such receivers. In the service building (building 15), the same receivers depend not only on the user, but also on tasks performed during the day, conditioned by working hours. ...
Building energy efficiency has grown strong in a context of soaring energy prices, especially in Europe. The use of energy-saving devices strongly influences its improvement, but in many cases, it is far from sufficient., especially if the energy comes from renewable sources with forced production. In the case of buildings, these are usually photovoltaic (PV) sources. For this reason, energy management systems (EMS) are becoming increasingly popular as they allow the increase in self-consumption and reduce the size of energy storage. This article presents analyses of historical energy consumption profiles in selected small- and medium-sized buildings powered by renewable energy sources. The implementation limitations of this type of systems, depending on the profile of the building, were identified and guidelines were presented to assess low-cost solutions dedicated to small buildings and considering the actual conditions of existing systems. Statistical analyzes were conducted for the energy demand profiles of 15 different buildings. The analyzes consisted of the preparation of box plots for each hour of working days and the calculation of the relative standard deviation (RSD) index for annual profiles of 60 min periods. The analyzes showed that the RSD index has low values for commercial buildings (e.g., hospital 7% and bank 15%) and very high values for residential buildings—even over 100%. On this basis, it can be concluded about the usefulness of energy profiles for demand forecasting. The novelty of the proposed method is to examine the possibility of using measurement data as data to forecast energy consumption based on statistical analysis, dedicated to low-cost EMS system solutions.
... This is not needed if all the loads are measured individually in the case of ILM. Currently available Smart Plugs on the market lack load classification capabilities [7], but the load-controlling features are already beneficial for accessibility [8]. A number of different Smart-Plug-based load classification solutions are proposed in the literature. ...
Full utilization of renewable energy resources is a difficult task due to the constantly changing demand-side load of the electrical grid. Demand-side management would solve this crucial problem. To enable demand-side management, knowledge about the composition of the grid load is required, as well as the ability to schedule individual loads. There are proposed Smart Plugs presented in the literature capable of such tasks. The problem, however, is that these methods lack the ability to detect if a previously unseen electrical load is connected. Misclassification of such loads presents a problem for load estimation and scheduling. Open-set recognition methods solve this problem by providing a way to detect samples not belonging to any class used during the training of the classifier. This paper evaluates the novel application of open-set recognition methods to the problem of load classification. Two approaches were examined, and both offer promising results. A Support Vector Machine based approach was first evaluated. The second, more robust method used a modified OpenMax-based algorithm to detect unseen loads.
... So, it is an electronic device that allows the user to obtain real-time data of electricity consumption of the electric appliance or device, which is plugged in using a web panel or mobile application. The plugs also convert the appliances into smart devices to control them remotely [15], [16]. ...
... Comparison of existing systems[5] ...
The purpose of the task is to construct a prototype of the Smart Power Extender and bridge the distance between the conventional and futuristic extension board. The proposed machine includes a microcontroller-primarily based improvement board that controls this prototype. Also, this prototype presents real-time tracking of the equipment's utilization to a Backend Server that manages the Internet of Things (IoT). This prototype also includes different protection functions provided by app interface, allowing the equipment to run most straightforwardly for a selected time limit. These functions permit the consumer to govern and protect the home equipment linked to it everywhere around the sector with the Internet's assistance.
