# Wireless Home Energy Management System with Smart Rule-Based Controller
## 1 Introduction
The main shortcoming of the work was the use of limited constraints (only room and water temperatures) in the optimization. In a related effort, [PERSON] et al. [12] developed a programming model for solving the mixed-objective optimization problem of energy usage and user comfort. Instead of using smart meter data, the authors assumed that the required data could be acquired from users and local energy resources, such as PV systems. Although the simulation algorithm was used to decrease the energy consumption and user satisfaction, in practice the work did not consider manual bypass by the user [12]. Recently, reinforcement learning (RL), which is a Markov decision process based technique, has been applied in the EMS problems. Q-learning RL algorithm was applied in [13] to automatically schedule appliance tasks according to the demand response signals as well as customers' requests and evaluations. However, the Q-learning RL discards the current data sample after every update, leading to the inefficient use of data. A detailed review that discusses the demand response schemes, the communication protocols, and the scheduling controller techniques, which are all required for the intelligent home EMSs, can be found in [14]. In this work, the scheduling controller techniques can be classified into rule-based, artificial intelligence (AI)-based, and optimization-based techniques. References [15,16] bridge the gap between the simulations and practical implementation of HEM systems. The authors in [15] developed a test bed for the scheduling of lighting loads attached to a smart plug. The control scheme was based on the power consumption information received at the controller terminal, which, in turn, actuates the on/off commands for the loads. However, the work did not consider other important loads, such as thermal loads, which are major energy consumers and user comfort factors in practical households [15,17] applied a binary backtracking search algorithm to optimally schedule and control various appliances using wireless smart sockets in order to reduce customer energy consumption during peak hours on real time basis. As an advancement on previous works, [18] presented a comprehensive HEM algorithm that considered distributed generation and a battery storage system (BSS). The aim of the controller was the optimization of the utilization schedule through battery charging and discharge to minimize the daily energy cost of the household. The main contribution of the research was its consideration of a large number of home appliances and the use of real data to simulate multiple energy usage scenarios. A practical implementation of an energy management systems consisting of a home server and Zigbee-connected home appliances was also proposed in [19]. The server acquired weather forecast information from the Internet and used the data in order to estimate the required energy generation from connected distributed generators (DGs). A user interface was established in order to display the previous load profile and the generation history of the DGs, together with weather forecast information. However, the developed test bed could not be used to monitor the information required for the maintenance of user comfort, such as room temperature, thermal appliance set points, and water temperature in the electrical heater, which can also be used to improve the efficiency of an HEM system [19]. In the meantime, several companies have designed and sell home automation systems. For example, General Electric Co. [20], Honda [21], Samsung [22], Fibaro [23], and etc. designed a system that controls home appliances using smartphones. These systems offer strong support to customers by controlling and monitoring devices with smartphone applications. However, the main disadvantage of commercial home automation systems are that the details of their software and hardware are confidential and unavailable for academic research.