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The present paper presents a new kind of Smart Plug that covers the needs of power systems R&D centers. EnAPlug, described in this paper, enables the monitor and control of loads, as a normal Smart Plug. However, it has a great benefit in comparison with a normal Smart Plug, the EnAPlug allows the integration of a variety of sensors so the user can...
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Citations
... Mataloto et al. (2019) also included photo-resistor sensors and motion sensors with passive infrared (PIR) in their custom sensor-board and Reddy et al. (2016), used a light dependent resistor (LDR) that reduces its resistance when light hits the surface of it. Gomes et al. (2017) created EnAPlug (Fig. 4), a multi-sensor smart plug with the ability to switch on/off devices, and monitor power, reactive power, voltage and current. It also included four sensors for temperature, humidity, outside temperature and a door opener detector. ...
... Last, another Aeon Gen6 multi-sensor was installed that could also provide ultraviolet light sensing data and also home energy meter was installed in the fuse box to measure instant consumption and energy for the entire home without noise. Fig. 4 EnAPlug implementation, a custom multi-sensor smart plug by Gomes et al. (2017) Content courtesy of Springer Nature, terms of use apply. Rights reserved. ...
Climate change has become a major problem for humanity in the last two decades. One of the reasons that caused it, is our daily energy waste. People consume electricity in order to use home/work appliances and devices and also reach certain levels of comfort while working or being at home. However, even though the environmental impact of this behavior is not immediately observed, it leads to increased CO2 emissions coming from energy generation from power plants. It has been shown that about 40% of these emissions come from the electricity consumption and also that about 20% of this percentage could have been saved if we started using energy more efficiently. Confronting such a problem efficiently will affect both the environment and our society. Monitoring energy consumption in real-time, changing energy wastage behavior of occupants and using automations with incorporated energy savings scenarios, are ways to decrease global energy footprint. In this review, we study intelligent systems for energy management in residential, commercial and educational buildings, classifying them in two major categories depending on whether they provide direct or indirect control. The article also discusses what the strengths and weaknesses are, which optimization techniques do they use and finally, provide insights about how these systems can be improved in the future.
... Mataloto et al. (2019) also included photoresistor sensors and motion sensors with passive infrared (PIR) in their custom sensor-board and Reddy et al. (2016), used a light dependent resistor (LDR) that reduces its resistance when light hits the surface of it. Gomes, Sousa, and Vale (2017) created EnAPlug, a multi-sensor smart plug with the ability to switch on/off devices, and monitor power, reactive power, voltage and current. It also included four sensors for temperature, humidity, outside temperature and a door opener detector. ...
Climate change has become a major problem for humanity in the last two decades. One of the reasons that caused it, is our daily energy waste. People consume electricity in order to use home/work appliances and devices and also reach certain levels of comfort while working or being at home. However, even though the environmental impact of this behavior is not immediately observed, it leads to increased CO2 emissions coming from energy generation from power plants. Confronting such a problem efficiently will affect both the environment and our society. Monitoring energy consumption in real-time, changing energy wastage behavior of occupants and using automations with incorporated energy savings scenarios, are ways to decrease global energy footprint. In this review, we study intelligent systems for energy management in residential, commercial and educational buildings, classifying them in two major categories depending on whether they provide direct or indirect control. The article also discusses what the strengths and weaknesses are, which optimization techniques do they use and finally, provide insights about how these systems can be improved in the future.
... (Obaidat et al., 2018) use the contextawareness concepts to create a framework for the intelligent data management in smart grids. (Gomes et al., 2017) as well as (Stavropoulos et al., 2014) use context-awareness for the development of energy management systems of households. (Degha et al., 2019) propose a solution for efficient energy management based on ontological context to smart building. ...
... EnAPlug was introduced, in 2017, in Reference [11]. The first proposed EnAPlug version was almost a pure hardware device without the capability of acting by itself. ...
Smart home devices currently available on the market can be used for remote monitoring and control. Energy management systems can take advantage of this and deploy solutions that can be implemented in our homes. One of the big enablers is smart plugs that allow the control of electrical resources while providing a retrofitting solution, hence avoiding the need for replacing the electrical devices. However, current so-called smart plugs lack the ability to understand the environment they are in, or the electrical appliance/resource they are controlling. This paper applies environment awareness smart plugs (EnAPlugs) able to provide enough data for energy management systems or act on its own, via a multi-agent approach. A case study is presented, which shows the application of the proposed approach in a house where 17 EnAPlugs are deployed. Results show the ability to shared knowledge and perform individual resource optimizations. This paper evidences that by integrating artificial intelligence on devices, energy advantages can be observed and used in favor of users, providing comfort and savings.
... This paper proposes an evolution of Environmental Awareness Smart Plug (EnAPlug) that is focused on the context. The previous version of EnAPlug was made using a microcontroller and worked as a passive system [15,16]. The proposed evolution is not only able to understand the controllable resource's context but also to learn from the resource consumption history and from the interaction between users and resources. ...
... Examples of low-cost and small SBC, with integrated Wi-Fi, are Raspberry Pi Zero, Orange Pi Zero, and NanoPi NEO Core2. This paper will focus on the software architecture, for the complementary hardware-sensors, actuators, and energy analyzer-please refer to [15,16]. ...
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 and communicate information regarding the resource’s context. This paper proposes the EnAPlug, a new environmental awareness smart plug with knowledge capabilities concerning the context of where and how users utilize a controllable resource. This paper will focus on the abilities to learn and to share knowledge between different EnAPlugs. The EnAPlug is tested in two different case studies where user habits and consumption profiles are learned. A case study for distributed resource optimization is also shown, where a central heater is optimized according to the shared knowledge of five EnAPlugs.
... In Fig.1 is possible to see the separation between the devices inside the building and the microgrid players represented by the agents. The devices used in this work are one of three types: IoT device available on the market, EnAPlug that is a IoT smart plug with environment awareness capabilities [13], or Virtual to Reality (V2R) emulator that is able to emulate electrical loads [14]. Each agent of µGIM represents an abstraction layer that separates the inside of the building from the outside. ...
... To overcome the current limitations of smart plugs, this work integrates EnAPlugs for some of the electrical loads. EnAPlug is an environmental awareness smart plug composed by multiple sensors that enable the understanding of the environment [13]. This enables the system to understand the environment were a specific load is, and not only its consumptions or operation stateif it is on or off. ...
... In Fig.1 is possible to see the separation between the devices inside the building and the microgrid players represented by the agents. The devices used in this work are one of three types: IoT device available on the market, EnAPlug that is a IoT smart plug with environment awareness capabilities [13], or Virtual to Reality (V2R) emulator that is able to emulate electrical loads [14]. Each agent of µGIM represents an abstraction layer that separates the inside of the building from the outside. ...
... To overcome the current limitations of smart plugs, this work integrates EnAPlugs for some of the electrical loads. EnAPlug is an environmental awareness smart plug composed by multiple sensors that enable the understanding of the environment [13]. This enables the system to understand the environment were a specific load is, and not only its consumptions or operation stateif it is on or off. ...
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.
