Internet of Things Enabled Intelligent Automation for Smart Home with the Integration of PSO Algorithm and PID Controller

Singh, Rajesh; Gehlot, Anita; Kuchhal, Piyush; Choudhury, Sushabhan; Akram, Shaik Vaseem; Priyadarshi, Neeraj; Khan, Baseem

doi:https://doi.org/10.1155/2023/9611321

Journal of Electrical and Computer Engineering

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Abstract Introduction Literature Review Results and Analysis Conclusion Data Availability Conflicts of Interest References Copyright Related Articles

Research Article | Open Access

Volume 2023 | Article ID 9611321 | https://doi.org/10.1155/2023/9611321

Internet of Things Enabled Intelligent Automation for Smart Home with the Integration of PSO Algorithm and PID Controller

Rajesh Singh,^1,2Anita Gehlot,^1,2Piyush Kuchhal,³Sushabhan Choudhury,³Shaik Vaseem Akram,^1,4Neeraj Priyadarshi,⁵and Baseem Khan^2,6

Academic Editor: Mohamed Louzazni

Received12 May 2022

Revised15 Feb 2023

Accepted25 Feb 2023

Published28 Mar 2023

Abstract

Currently, due to the widespread population growth, there is a widespread concern about an electricity shortage. As a result, smart devices have evolved and gained significant attention to reduce power consumption in home appliances due to electricity shortages. However, it lacks a universal remote control that can control home appliances based on environmental conditions. To overcome these challenges, this study proposed a hardware-based remote-control system that operates both in autonomous and semiautonomous modes to control home appliances based on environmental conditions. In the autonomous mode, the receiver section regulates the parameters under ambient conditions by varying the appliance’s applied voltage levels via a dimmer. The parameters in semiautonomous are monitored by the user via various levels of remote control. A 2.4 GHz RF modem is used to establish wireless personal network (WPAN) communication between the remote and the receiver. In addition, a Wi-Fi modem is built into the receiver to enable internet-based mobile applications to operate appliances. During the MATLAB analysis, a proportional integral derivative (PID) controller with a particle swarm optimization (PSO) method was found as a superior approach to control the home appliance with adequate environmental conditions. It is concluded from the MATLAB study that the PSO-PID controller delivered an energy saving of 14.88% for the heater, 36.9% for the exhaust fan, and 37.49% for the light bulb compared to the conventional appliances.

1. Introduction

At present, India is one of the top three countries in terms of energy generation, with 1,547 Terawatt-hour (TWh) and 1,309 TWh consumption, respectively [1, 2]. Over the last decade, electricity consumption has increased by 73% due to the rapid population growth and it led to the power shortage faced by India, i.e., 70,000 Mega Watt (MW) [3]. The challenge of power shortages can be handled in two ways as follows: the first is to employ more traditional renewable energy sources to generate more electricity [4] and the second is to optimize the use of available electricity by smart appliances [5]. Technological advancements have empowered the development of smart devices not only to minimize power usage but also energy waste. Integrating smart devices into the home environment [6] led to the evolution of smart home automation in which devices are automatically controlled. Yet, there is a lack of common protocol to control multiple devices through a single controller with reliability and flexibility in home automation [2]. A home is similar to a system with several physical parameters that depend on each other and the environment. It is observed as a subsystem that exhibits linear or nonlinear dynamic behaviors [7]. The complexity of the control operation increases as there are changes in the internal behavior and environment in the building. Due to growing environmental concerns, control systems play an important role in reducing energy consumption without compromising comfort goals [8].

A control system is a set of commands to regulate the behavior of a device, and a control system can be open-loop or closed-loop [9]. In previous studies, the control system is interconnected with the components of the home to control the appliances. However, this approach resulted in unreliable, inaccurate performance and is difficult to sense the changes in the external environment [10]. To overcome these limitations, open-loop and closed-loop controls are used. Open-loop control shows the actual operation of the system according to the input signal, but a closed-loop system is used for the desired output with different input signals [11]. A PID controller is utilized as a control system to control appliances by tuning the parameters [12, 13].

Reference [14] concludes that optimization is a critical notion in engineering for realizing the end product to reduce power consumption, boost yield, cost, and time delay. Moreover, optimization is carried out in the field of engineering because of challenges due to discontinuity, nonlinearity, and nondifferentiability. In reference [15], to obtain sufficient matching factors for the system’s derivation, many computational operations are essential for hardware devices. Many optimization techniques are implemented to minimize computational complexity. For hardware-compatible optimization methodologies, genetic algorithm (GA) and PSO algorithms have been considered for determining the right tuning parameters. The authors in [16] conducted a study in which they integrated PSO and GA-PSO to create two resource allocation techniques to increase system performance by enabling up to two device-device pairs to share the same frequency with one cellular user equipment. In reference [17], the analytical framework is used to capture a wide range of properties of optimization algorithm implementations in PSO hardware (Field Programmable Gate Arrays) and software, with a focus on outperforming similar state-of-the-art implementations. It is also concluded that optimization algorithms can be adopted in the hardware devices for overcoming the issues faced due to discontinuity, nonlinearity, and nondifferentiability. As a part of controlling home appliances, a PID controller is implemented for tuning the parameters in order of implementing the autonomous and semiautonomous mode-based system.

With the motivation of the previous facts, this study aims to implement an intelligent remote-control system with hardware to control home appliances in autonomous and semiautonomous modes, based on environmental conditions variation. A MATLAB analysis is carried out to conclude which PID controller based on different algorithms can be implemented in the hardware for autonomous and semiautonomous modes. The study implemented the GA and PSO algorithms to check the optimal optimization algorithm for implementing the PID controller effectively for autonomous and semiautonomous modes for controlling home appliances. The novelty of the proposed system is implementing the customized hardware system for controlling the appliances with a PID controller and tuning parameters for implementing a semiautonomous mode. The contribution of the study is as follows:(i)Customized hardware with 2.4 GHz RF and Wi-Fi communication capability is implemented for wireless connectivity establishment between remote and receiver sections for autonomous and semiautonomous operation.(ii)A MATLAB analysis is carried out and identified which particular optimization algorithms are optimal in tuning parameters of hardware.(iii)The experimental results conclude that the PSO-PID controller delivered an energy saving of 14.88% for the heater, 36.9% for the exhaust fan, and 37.49% for the light bulb compared to conventional appliances.(iv)The current consumption analysis of remote and receiver hardware is also analyzed in this study.

The structure of the study is as follows: Section 2 is a review of the literature; Section 3 is a detailed description of the working of the system using block diagrams. Section 4 covers the PID controller and optimization algorithm. Section 5 covers the programming flow diagram of semiautonomous mode, autonomous mode, and receiver. Section 6 covers the calibration of sensors with instruments. Section 7 covers the experimental outcomes of a heater, bulb, and exhaust fan along with power consumption analysis. Section 8 covers a conclusion and the future scope of the system.

2. Literature Review

In this section, we discuss the distinct techniques that have been implemented in the home to control the appliances and minimize energy consumption. A wireless sensor network-based thermal comfort system is implemented to maintain room temperature as the environment changes, and results are obtained with neuro-fuzzy and PSO [18]. An intelligent monitoring system has been implemented to monitor various parameters of the home environment by integrating Zigbee and Global System for Mobile Communication (GSM) technology [19]. The PID controller was modified to use PSO with an automatic voltage change that has increased the system's efficiency [20]. A method is proposed to achieve an efficient output voltage or high switching frequency based on an inverter and the PID methods with the lowest ripple content [21, 22]. A comparative study of ant colony optimizations (ACOs) using the genetic algorithm and the Ziegler–Nichols method was discussed and concluded that ACOs are a better option [23]. To efficiently manage energy consumption, an android smartphone-based remote control for the home energy management system is proposed to dim the liquid emitting diode (LED) [21, 24]. A genetic algorithm for automatic control of PID controller in heating, ventilation, and air conditioning (HVAC) systems is carried out, and it is concluded that the genetic algorithm delivered the best performance [25]. A method was suggested by a study to determine which of the three optimization methods is best for tuning PIDC controllers for automatic voltage regulators [26].

A ZigBee-based smart energy management system is proposed with the help of a motion sensor and power consumption settling time to reduce power consumption [27, 28]. IoT-based and ZigBee-based wireless sensor network (WSN) is implemented to monitor environmental parameters such as temperature, humidity, meter readings, and light [29, 30]. An integrated WSN with bee moths and Meshlium gateway was implemented to conserve energy by regulating light strength with pulse width modulation (PWM) [31]. IoT talk remote control (IoTtalkRC) is a mechanism that uses sensors to replace infrared remote controls as universal software-defined remote controls for aftermarket appliances [32, 33]. A prototype has been developed to perform remote control and performance evaluation of ECHONET-compatible smart home devices [34, 35]. GSM and Bluetooth-enabled remote monitoring system is developed through an android-based graphical user interface (GUI) [36, 37]. Open NFC touch and control (NTCP) is a tool that allows users to replace home appliances and devices of home appliances with smartphones to use universal remote controls [38]. Intelligent remote control, which controls and operates all home devices via the Internet via a mobile app, has independent learning capabilities and acts as a microcontroller for the ESP8266 [39]. With the integration of Wi-Fi and radio frequency, a voice- and wireless-based smart home management system is presented. To make the system more approachable, two sets of control schemes—voice control and button control—are created by the functional requirements of the system [40]. An IoT-inspired intelligent smart home control system is implemented to monitor and control the security of the home through a mobile application [41, 42]. In reference [43], authors presented the design and implementation of the energy efficient home automation system. Table 1 presents the comparative analysis of the current study, with the other studies that implemented the home automation-based system with IoT. It has been observed that the most recent studies have implemented the Wi-Fi-based module such as ESP 8266, which has limitations in power consumption. The significant parameter that is included in the comparative table is a mode of automation, where semiautonomous-based automation is implemented in a wide range. However, a current study implemented a network of 2.4 GHz RF and Wi-Fi for the implementation of autonomous and semiautonomous systems in controlling home appliances. Moreover, this study has calibrated the sensor and also optimized the proposed hardware as it is limitedly implemented in the previous studies.

From the literature review, it is concluded that the PID controller can be utilized to control the home appliance system. The power consumption is minimized by using optimization techniques with a PID controller. Moreover, it is concluded from the review that the ZigBee network can be used to implement the autonomous mode. It is also concluded that optimization algorithms can be adopted in the hardware devices for overcoming the issues faced due to discontinuity, nonlinearity, and nondifferentiability.

3. System Development

An IoT-assisted architecture is proposed to implement remote control in autonomous and semiautonomous modes (Figure 1). The system is divided into two parts, a receiver section and the remote control. The remote control is used to control the receiver section, connected to the appliances to be controlled. In autonomous mode, the receiver section controls the parameters under ambient circumstances by varying the applied voltage levels of the appliance through a dimmer.

In semiautonomous, the parameters are monitored by the user via various levels of remote control. A 2.4 GHz RF modem is used to establish wireless personal network (WPAN) communication between the remote and the receiver. A Wi-Fi modem is integrated with the receiver to activate the control of appliances through the mobile application based on the Internet. The significance and function of the receiver section and remote section are discussed in the following section with a block diagram.

3.1. Remote Control

The remote control shown in Figure 2 comprises of temperature/humidity (T/H) sensor, light-dependent resistors (LDRs), ATmega16, RF modem, LCD 16 2 display unit, switches, and battery. The switch array is used to control the appliances. The temperature/humidity sensor provides temperature and humidity information as a feedback signal to the system. The remote control has nine switches for various operations.

Two switches are for mode selection of operation as semiautonomous or autonomous mode. Three pairs of up/down keys are kept in the remote control to increase/decrease the three parameters, namely, exhaust fan speed, heater temperature, and light intensity. The “OK” switch is used to deliver the information packet to the receiver section, and switch multiplexing is utilized to reduce the number of switches by using the same keys for level shifting on all appliances. A li-ion battery is used as a power supply module for remote control, and all the devices at the remote-control end operate at a +5 V DC power supply.

3.2. Receiver Section

Figure 3 shows the receiver board comprised of a three-dimmer circuit, ATmega 16 microcontroller, RF modem, LCD 16 2 display unit, power supply, exhaust fan (EF), heater (H), and bulb (B). Dimming circuits are useful to control the dimming levels of the exhaust fan, heater, and bulb.

PID controller is implemented with a dimmer in the receiver section to control the dimming of room appliances, by setting the firing angle. The receiver section will receive a packet that contains the data from the remote control in the form of temperature, humidity, and light intensity. If the packet contains the data of temperature, then the corresponding error signal is generated and then provides the signal to the PID controller to control the temperature of the heater. The same is the case with the humidity and light intensity. In addition, the receiver section is connected to the mobile app through the Internet and cloud server. The mobile app is also utilized to control home appliances based on the environmental conditions of the home and the outer environment.

4. PID Controller with the Algorithm

The system is tuned to obtain the tuning parameters of PID with MATLAB Simulink, for a desired closed-loop response. The PID controller’s system function is provided by the following equation:where is the proportional gain, is the integral gain, and is the derivative gain.

The PID control system is optimized by adjusting certain performance indexes. The performance index is determined over a time interval “T.” The performance indices [11] such as integral absolute error (IAE), integral square error (ISE), integral time absolute error (ITAE), and integral square time error (ISTE) are provided in the following equations:

4.1. Optimization Algorithms

Many computing processes are required to achieve suitable matching factors for the system’s derivation. To avoid computing complexity, many optimization approaches are applied. For finding the appropriate tuning settings, genetic algorithm (GA) and PSO algorithms have been considered for hardware-compatible optimization strategies.

4.1.1. Genetic Algorithm

GA is based on natural selection and genetic developmental theories, and it uses prior knowledge to route searches toward areas with greater search performance. Figure 4 shows the flow chart for the GA. The algorithm steps are as follows: initialization of the population of an individual, evaluation of fitness-by-fitness function, selection of fitness members of the population, application of mutation process, and best chromosome selection process when termination criteria are completed, so if not, search for another set of the best chromosomes.

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Figure 5 represents the implementation of GA with the PID controller. A GA to determine the value of the PID controller’s tuning parameters is called the GA-PID controller. The error signal is generated from the difference between the input value of the remote control and the feedback signal from the sensor. The device is connected to the controller via a dimming circuit. The height at which the trigger occurs is determined by the controller. GA helps to calculate optimized tuning parameters to control the PID controller, and the trigger level is determined based on the tuning parameter error signal.

4.1.2. PSO Algorithm

Eberhart and Kennedy suggested PSO in 1995. The birds’ social behavior underlying the algorithm is the key notion. The particles travel at an initial speed according to the goal surface and attempt to change their speeds and locations on a local basis after each iteration. The steps involved in the PSO algorithm are as follows: enter all of the desired constraints, system functions, and PSO constants, as well as the number of particles. Set the particle positions and velocities to zero. Evaluate the system’s fitness value with unit step response, calculate system constraints for each particle, and compare the individual fitness value of each particle to its previous value; if it is better than the previous one, replace it with a new value; otherwise do not change; the position of a particle with the lowest error is the global best value, particle position and velocity should be updated by equation (1), return to step 3, and repeat until all system constraints are met. Table 2 illustrates the parameters selected for the PSO algorithm and the equation for the optimization of parameters in the PSO algorithm.

Figure 6 shows PSO to find out the values for tuning parameters of the PID controller. The error signal is generated with the difference between the input value from the remote control and the feedback signal from the sensor.

5. Flow Diagram

In this section, we present the flow diagram of the semiautonomous mode, autonomous mode, and receiver.

5.1. Semiautonomous Mode

Figure 7 shows the flowchart, which discusses all the programming steps to develop the firmware of the remote control in semiautonomous mode, with embedded “C.” The functions for liquid crystal display (LCD), universal synchronous and asynchronous receiver transmitter (USART), and analog-to-digital converter (ADC) are initialized. The controller waits for an interrupt signal after the remote control has been initialized.

When the interrupt signal is received, the user will have the option to select the mode of operation. If semiautonomous mode is selected, then values need to be predefined by the user through switches available on the remote control. After selecting the mode of operation, the controller will wait for another signal. Combinations of switches on the remote control are available for each appliance to select the device’s dimming levels, and the signal generated by pressing the proper combination of switches will be processed through the controller. There are nine switches for the different functions of the controller. The “OK” switch is pressed to send the data in the form of a data packet with a unique ID to the receiver section. The same data are displayed on the LCD placed on the remote control.

5.2. Autonomous Mode

Figure 8 shows the flowchart to discuss the steps involved in developing firmware for remote control in autonomous mode, with embedded “C” language. The functions for LCD, USART, and ADC are initialized. When the interrupt signal is received, the user will have the option of selecting the mode of operation. After the initialization of the receiver section, the controller waits for an interrupt signal. Here, remote control plays a significant role in the maintenance of ambient conditions in autonomous mode. The user must only supply physical parameter values like temperature, humidity, and light intensity via the switch array. The sensor value (signal-to-empty) measured by the user and input by the user (signal-to-empty section reference) is sent to the receiver section through a ZigBee packet.

5.3. Receiver

Figure 9(a) shows the interrupt subroutine, and Figure 9(b) shows the flowchart to discuss the steps involved in developing firmware for the receiver section, in embedded “C” language. In two separate modes, the recipient portion performs various functions. In semiautonomous, the dimmer level of the appliance must only be determined by the settings specified by the user on the remote control. In stand-alone mode, however, the receiver generates an error signal by subtracting the sensor value from the remote-control input value. For the method employed with the PID controller, this error signal is used as an objective function. The PID, GA-PID, and PSO-PID values are calculated to calculate , , and . The device is then activated at the required level by assessing the system’s requirements for moving levels up and down. After selecting the appropriate dimming level, the appliance is to work on the directed voltage level. In the developed system, three appliances (heater, exhaust fan, and bulb) have been controlled.

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6. Calibration of Sensors

The section describes the calibration of the sensors used for system development. The calibration and testing of sensors are done with the help of standard instruments, and results are verified. The dimming methodology is also discussed in detail, and power consumption for each level is calculated.

6.1. Temperature/Humidity Sensor

The temperature/humidity sensor is calibrated using a standard instrument called a psychrometer. It is necessary to use an intelligent system because, as time passes, the prior temperature rises. Once the system has reached the desired temperature, it must be lowered after some time to maintain a constant temperature. To analyze temperature and humidity level control for the system, a temperature/humidity sensor is used. The temperature/humidity sensor is connected in USART mode with a microcontroller. The values obtained for calibrating the humidity and temperature sensor are as presented in Table 3.

Figure 10(a) shows the comparison between relative humidity values given by the sensor and the standard instrument. Figure 10(b) shows that there is no error in the temperature value given by the sensor and standard instrument, as both values are overlapped. However, there is a slight difference between humidity values, which overcomes with the help of programming, and calibration for the temperature/humidity sensor is achieved accurately. After calibration, testing is also performed with standard instruments. For temperature testing, a thermometer is used, and for humidity testing, a humidity cabinet is used as shown in Figures 11(a) and 11(b).

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6.2. LDR Sensor

The resistance of an LDR deviates from the sum of light that strikes it. The relationship between the light intensity lux for a typical LDR and load resistance (R_L) is as given in the following equation:

With the LDR connected to 5 V through a 10 K resistor, the output voltage of the LDR is as follows:

Table 4 presents the values related to the relationship between resistance of LDR and with % illuminance level. The relationship between the resistance and light intensity is illustrated in Figure 12(a). LDR performance has been experimentally analyzed, and the result shows that calibration of the system is accurately achieved. Table 5 presents the values of the relation between lux and LDR % illuminance level. The relationship between the resistance values of LDR with the % illuminance level is presented in the table. Figure 12(b) shows the relation between the resistance values of LDR with the % illuminance level. The LDR gives the output as an analog signal, so it is connected to the ADC pin of a microcontroller.

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6.3. Operation of Dimming System

In the dimmer circuit, the alternating current (AC) phase control method is used to control the intensity of the appliance. Concerning the firing angle of a triac, the RMS value of the voltage provided to the appliance is varied. The RMS voltage provided to the load changes as the firing angle is adjusted, and the voltage dimming level of the appliance is adjusted accordingly. The formula for firing angle calculation is expressed in the following equation:where is the RMS value of output voltage; is the applied voltage; and α is the firing angle.

From the formula given in equation (8), it is concluded that to control the dimming level of the appliance, triggering is required in different 16 levels as shown in Table 6. Figure 13 shows the variation in w.r.t firing angle and it can be observed that with an increase in firing angle, decreases.

According to firing angle and values for dimming levels, current is measured and the total power consumption is calculated. Table 7 shows the voltage, current, and power consumed by the bulb, exhaust fan, and heater at different dimming levels according to firing angle. It is observed that with an increase in level, the power consumption is also increasing, and appliances consume more power at higher levels.

Figure 14(a) illustrates the graph of the power consumption by 100 W bulb concerning dimming level. Figure 14(b) illustrates the graph of power consumption by 18 W exhaust fan concerning dimming level, and Figure 14(c) shows the power consumption by 1 KW heater for all the sixteen dimming levels.

7. Results and Analysis

In this section, the experimental outcomes of the research are explained with the analysis for each appliance. Figures 15(a) and 15(b) present the receiver section, where there are three sockets on the board that correspond to the appliances such as the bulb, the heater, and the exhaust fan. The dimmer circuits are designed for controlling the light intensity, humidity, and temperature of the room. The system is tested in a room size of 10 8 10 cubic feet, with a 100-Watt bulb, 1 KW room heater, and 18-Watt exhaust fan. In semiautonomous, dimming levels of appliances are controlled by setting the required level, with remote control as per the user’s desire, and remote control in the semiautonomous mode is shown in Figure 15(c). In the autonomous mode, the dimming level is controlled by an error signal generated from the difference between sensors of the remote control. The input value of the parameter set by the user and the remote control in the autonomous mode is shown in Figure 15(d). The sensors are calibrated with standard instruments before implementation on the system. The standard instrument for light intensity is a lux meter, and for humidity and temperature, the psychrometer is used. The results are analyzed in terms of percentage saving of power w.r.t conventional appliances. The experimental outcomes of the heater, bulb, and exhaust fan are presented by implementing three different techniques, namely, PID, GA-PID, and PSO-PSD.

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7.1. Experiment Outcomes for Heater

The tuning parameters are calculated for all three methods with the MATLAB tool using PID, GA-PID, and PSO-PID. An experiment is conducted in a room size of 10 8 10, and the performance of every appliance is analyzed individually. As per the standards, a 1 KW heater is sufficient for this size of the room. The experiment is carried out with 1 KW in a room size of 10 × 8 × 10 in March 2020, for two hours, with the initial temperature observed as 29°C. The target is to maintain the room temperature at 30°C.

Figure 16 shows the power consumption of the heater during the experimental period; during the first 30 minutes, all methods use the same power as the system to reach the set temperature value. It is observed that in half an hour (8:30 A.M to 9:00 A.M), there is a 1°C of temperature rise, as the target was to maintain 30°C with 29°C initial temperature. So, the system consumed the same power as the conventional heater. After reaching a set value of temperature, the temperature starts increasing due to the effect of the external environment. To nullify this effect, the system is triggered at the lower level. Now, the temperature drops, and to maintain a required level, the system is triggered at a higher level.

Power analysis is performed using PID, GA-PID, and PSO-PID to keep the heater power consumption at a temperature of 300°C and the initial temperature at 29°C. It is concluded that power consumption by the conventional heater is 1.8 KW for two hours. Power consumption by the heater with PID controller is 1.703 KW, with GA-PID it is 1.605 KW, and with PSO-PIS it is 1.532 KW. The power saving w.r.t conventional heater is calculated in each case, and it is shown in Table 8. Figure 17 shows that the power efficiency for PID is 5.38%, for GA-PID it is 10.83%, and for PSO-PID it is 14.88%. Results clearly show that PSO-PID consumes less power than PID and GA-PID and is the best suited for the designed system.

7.2. Experiment Outcomes for Bulb

For bulb analysis, the values of PID parameters Kp, Ki, and Kd are tuned with PID, GA-PID, and PSO-PID. An experiment is conducted in a room size of 10 8 10 with 100 W, in March 2020 at home. An experiment is performed for four hours, with a background light intensity of 20 lux. The values from lux meter are in lux and from LDR in %. The target was to maintain the light intensity at 80%. PID, PSO-PID, and GA-PID are used to analyze the power usage of a 100 W bulb to keep the light intensity at 200 lux or 80 percent LDR with 20 percent initial intensity.

The energy meter power consumption is calculated and presented in Figure 18. It is concluded that power consumption by the conventional bulb is 400 W for four hours (with the assumption, it is switched off at 10:30 P.M). Power consumption with PID controller is 278.7 W, with GA-PID it is 253.1 W, and with PSO-PIS it is 250.02 W. The power saving w.r.t the conventional bulb was observed in each case. Figure 19 shows that the power saving for PID is 30%, for GA-PID saving is 37%, and for PSO-PID it is 37.49%. Results in Table 9 show that the PSO-PID method consumes less power than PID and GA-PID and it is best suited for the designed system.

7.3. Experiment Outcomes for Exhaust Fan

For exhaust fan analysis, the values of PID tuning parameters Kp, Ki, and Kd are tuned with PID, GA-PID, and PSO-PID. The tuning parameters are calculated for all three methods. The experiment is conducted in a room size of 10 8 10 with an 18 W exhaust fan, in March 2020. Experiment data are collected for four hours, with initial humidity of 44%. The target is to maintain the humidity at 42%. Based on readings taken with the energy meter; power is calculated and presented in Figure 20. Moreover, it is concluded that power consumption by a conventional exhaust fan is 72 W for four hours. Power consumption by exhaust with PID controller is 48.9 W, with GA-PID it is 47.5 W, and with PSO-PID it is 45.4 W.

Figure 21 shows that % of power saving for PSO-PID is 36.9%, PID is 32%, and GA-PID is 34%. Results in Table 10 clearly show that PSO-PID consumes less power than PID and GA-PID and is best suited for the designed system.

7.4. Current Consumption Analysis

In this section, we discuss the current consumption by receiver section and remote control in detail. Tables 11 and 12 show current consumption for both the remote control and receiver section in mA. Remote control consumes 99.5 mA of current, and the receiver section consumes 154 mA of current. Even though power was not a design issue for the system, it is evident that the designed system requires very little power.

The total power consumption by remote control comes out as (99.5 mA 5 V = 497.5 mW); the three components that dominate power consumption for remote control are the microcontroller, RF modem, and temperature/humidity sensor. The battery used in this application is a rechargeable lithium-ion battery with a potential of 12 V/1 A; thus, it can be used continuously (day/night) in the application for approximately 25 days. The receiver section is to be fitted with switchboards, so it takes power from the main supply through the regulator. The total current consumption by receiver section is shown in Table 12 and comes out to 154 mA.

8. Conclusion

The concern of electricity scarcity is prevalent due to the expansion of population growth. Smart devices are being widely implemented in the home to reduce power usage in home appliances owing to power shortages. However, there is still a lack of research that implements optimization techniques with remote controllers to reduce energy consumption in homes. To overcome these challenges, this study proposed a system that operates in autonomous and semiautonomous modes to control home appliances according to environmental conditions. Of the MATLAB analysis, PID-PSO algorithm is concluded as optimal to control the home appliance under suitable environmental conditions. Finally, from the experimental results, the PSO-PID controller delivered an energy saving of 14.88% for the heater, 36.9% for the exhaust fan, and 37.49% for the light bulb compared to the conventional appliances. A 2.4 GHz RF module is used to establish wireless personal network communication between the remote and the receiver. A Wi-Fi modem is integrated with the receiver to activate the control of appliances through the mobile application based on the Internet.

Data Availability

Data available on request from the authors.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

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