# Development of a Low-Cost Automated Demand Response Controller for Home Energy Management
## 1 Introduction
[PERSON] and [PERSON] [3] proposed a transactive controller installed on each customer in a distribution network to read locational market price signals and, after considering user preference for electricity, respond to his/her bidding strategy to the DR program by the smart plug-in. The controllable load in DR contained HVAC and shiftable load. Using the Monte Carlo simulation (MCS) method, a probability approach was used to evaluate the effectiveness of the overall energy and cost reduction while considering uncertain factors, such as load demand, user preference, environmental conditions, and home thermal behavior. However, hardware and software implementation of the so-called transactive controller is not discussed. An intelligent multi-objective energy management system (MOEMS) in a residential smart microgrid was proposed and simulated by [PERSON] et al. [4]. The home users were assumed to own intelligent appliances to participate in the DR program. The proposed MOEMS considered user satisfaction, and therefore, it conformed better to user demands than the traditional energy management system (merely considering costs). However, the design and implementation of DR controllers is not given. [PERSON] and [PERSON] [5] presented a fog-based Internet of Energy (IoE) architecture for transactive energy (TE) management systems. It supports communication protocols such as hypertext transfer, OpenADR, etc. However, they did not discuss demand response controllers for ADR. Several works on ADR management were reported using a genetic algorithm to reduce electricity cost and peak-to-average ratio [6], a mixed-integer linear optimization problem for scheduling controllable home appliances in the ADR program [7], and machine-learning techniques (reinforcement learning [8; 9; 10], Gaussian process, and logistic regression [2]) to learn customer DR behaviors with historical data. All these lack addressing the development of a low-cost demand controller. [PERSON] et al. [11] developed a smart meter to measure energy and communicate with OpenADR protocol using a Raspberry Pi, a microcontroller, and an energy-metering IC, making the smart meter a VEN (Virtual End Node). The ECHONET-Lite protocol was suggested to equip the VEN to control smart appliances. [PERSON] and [PERSON] (2013) developed an ECHONET-Lite gateway on a Raspberry Pi that acts as a VEN and can control ECHONET-Lite smart appliances. [PERSON] et al. [PERSON] et al. (2014) proposed a grid-interactive appliance controller (GIAC) integrating an IoT power quality sensor and an infrastructure based on the OpenADR standard. The IoT power quality sensor consists of current/voltage sensors, an energy measurement IC (MCP3909), and a system-on-chip (ESP32 SoC). The OpenADR was then employed through OpenLEADR. The demand control was conducted by sending control signals to the programmable source and loads. Therefore, these systems have to run with either ECHONET-Lite or OpenLEADR. [PERSON] et al. [PERSON] et al. (2018) also proposed an automatic metering infrastructure (AMI)-based online home ADR system compliant with OpenADR 2.0. However, the DR level of the air conditioner is determined by a particular duty cycle that is calculated in proportion to cumulative power consumption. Infrared control is not implemented; therefore, the temperature of the air conditioner (AC) and operating mode (fanning) cannot be easily controlled. Therefore, a cost-effective automated DRC compliant with the OpenADR 2.0a protocol, which could receive, analyze, respond to DR instructions, and process necessary control strategies, is essential for executing the decisions obtained from those demand response algorithms proposed in the abovementioned literature. The ability to use infrared remote control is essential to a successful ADR program, as is the participation of many old-installed air conditioners in homes and office buildings.