Residence Energy Control System Based on Wireless Smart Socket and IoT
To avoid resources on green earth being exhausted much earlier by human beings, energy saving has been one of the key issues in our everyday lives. In fact, energy control for some appliances is an effective method to save energy at home, since it prevents users from consuming too much energy. Even though there are numerous commercial energy-effective products that are helpful in energy saving for particular appliances, it is still hard to _nd a comprehensive solution to effectively reduce appliances’ energy consumption in a house. Therefore, in this paper, an intelligent energy control scheme, named the residence energy control system (RECoS) is proposed, which is developed based on wireless smart socket and Internet of Things technology to minimize energy consumption of home appliances without deploying sensors. The RECoS provides four control modes, including peak-time control, energy-limit control, automatic control, and user control. The former two are operated for all smart sockets in a house, while the latter two are used by individual smart sockets, aiming to enhance the functionality of energy control. The experimental results show that the proposed scheme can save up to 43.4% of energy for some appliances in one weekday.
Nowadays, many related studies of home energy control have been proposed. Mohsenian-Rad et al.  introduced a game-based approach for optimizing energy consumed by a residential building. But they did not consider users’ satisfaction degree for their ef_cient task scheduling. Optimal scheduling of in-home appliances with storage devices has been discussed in , in which the total cost minimization is one of the objectives of its optimization attempt. Basically, these two techniques were developed mainly based on deterministic and/or meta-heuristic methods. But they failed to consider users’ convenience and comfort levels for their cost optimization process. Anvari-Moghaddam et al.  developed an integer nonlinear programming model for optimal energy use in a smart home by considering a meaningful balance between energy saving and a comfortable lifestyle. Through incorporation of a mixed objective function under different system constraints and user preferences, the algorithm presented in  reduced the domestic energy usage and utility bills, and ensured an optimal task scheduling and a thermal comfort zone for its inhabitants. However, if IoT techniques can be applied to this model, the energy can be further reduced. The developments of the IoT and wireless sensor networks come up with new solutions for residence management. In such a home management system, a _x IP address is required, and remote users need a high-speed connection to access the system.
In this paper, an intelligent energy saving scheme, named the Residence Energy Control System (RECoS for short), is proposed to reduce the energy consumption of home appliances without deploying sensors. The RECoS, based on wireless smart sockets and IoT technology, not only monitors/controls the standby power consumption of an individual appliance, but also manages energy consumed by all controllable appliances. The RECoS also invokes the neural network algorithm to study user’s lifestyle and automatically turns off the power of each smart socket connected to IoT when the electric appliances are not in use. The experiments demonstrate that the RECoS can save up to 43% of energy for some appliances in a weekday.
One of the main purposes of constructing a smart house is to automatically control those appliances in the house to achieve the goals of energy saving and smart living. In this paper, the RECoS controls the energy consumption in a residence through IoT and smart sockets. The RECoS provides four control modes to control the on/off state of home appliances connected to smart sockets. A simple IoT structure which integrates smart sockets, home gateway, energy controller, ZigBee, and Internet is proposed. Most importantly, the RECoS is sensorless and can be applied to outdated appliances, i.e., those without providing network connections. By using the neural network algorithm for smart learning, the RECoS can save unnecessary energy consumed by a house, and the experimental results show that up to 43.4% of energy can be reduced for a water dispenser in a weekday. Other appliances can also save some amounts of energy. In the near future, the reliability and behaviour models ,  would be derived for the RECoS so that the users can, respectively, predict its reliability and behaviours before using it. Besides, a simple user interface and personalized learning model will also be developed so that the RECoS can reduce the energy consumption more intelligently. Furthermore, security is an important issue in safely protecting the RECoS, e.g., encrypting the control commands sent to smart sockets ,  to avoid hackers turning on/off the sockets that need to be turned off/on. These constitute the future studies.
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