IoT Operating System Based Fuzzy Inference System for Home Energy Management System in Smart Buildings

Abstract

Energy consumption in the residential sector is 25% of all the sectors. The advent of smart appliances and intelligent sensors have increased the realization of home energy management systems. Acquiring balance between energy consumption and user comfort is in the spotlight when the performance of the smart home is evaluated. Appliances of heating, ventilation and air conditioning constitute up to 64% of energy consumption in residential buildings. A number of research works have shown that fuzzy logic system integrated with other techniques is used with the main objective of energy consumption minimization. However, user comfort is often sacrificed in these techniques. In this paper, we have proposed a Fuzzy Inference System (FIS) that uses humidity as an additional input parameter in order to maintain the thermostat set-points according to user comfort. Additionally, we have used indoor room temperature variation as a feedback to proposed FIS in order to get the better energy consumption. As the number of rules increase, the task of defining them in FIS becomes time consuming and eventually increases the chance of manual errors. We have also proposed the automatic rule base generation using the combinatorial method. The proposed techniques are evaluated using Mamdani FIS and Sugeno FIS. The proposed method provides a flexible and energy efficient decision-making system that maintains the user thermal comfort with the help of intelligent sensors. The proposed FIS system requires less memory and low processing power along with the use of sensors, making it possible to be used in the IoT operating system e.g., RIOT. Simulation results validate that the proposed technique reduces energy consumption by 28%.

Sustainable Waste Management Drilling Process in Fuzzy Environment

“…Solve the problem of maximizing the objective function µ 0 (x) taking into account the constraints imposed in Equations (7) and (8), (9) and (10), and (11) and (12), determine the current solutions: x(β)-values of the control (regime) parameters, µ 0 (x(β)) criterion values; µ 1 (x(β)), . .…”

“…Since, when creating such systems, problems of shortage often arise, the fuzziness of the initial information [2] is especially important to develop and use a management system that is operable in a fuzzy environment [3]. Such systems are based on fuzzy information in the form of formalized with the methods of fuzzy set theory (FST), knowledge and experience of a person, expert decision maker (EDM) [3][4][5][6][7].…”

A Review of the Recent Developments in Integrating Machine Learning Models with Sensor Devices in the Smart Buildings Sector with a View to Attaining Enhanced Sensing, Energy Efficiency, and Optimal Building Management

“…With respect to the publication year, 63% of the identified articles were published during the last 5 years. The authors of these scientific articles made use in their analyses of different types of sensors, including sensors and actuators related to the primary heating circuits and power generation systems [24]; telecare medicine information systems (TMIS) comprising specialized sensors that provide key health data parameters [99]; distributed sensors [100]; temperature, humidity and flame sensors [101]; string-type strain gauges [49]; temperature and occupancy sensors [54]; wireless sensors [47,102]; environment sensors for measuring indoor illuminance, temperature-humidity, carbon dioxide concentration and outdoor rain and wind direction [103]; sensors for measuring the indoor and outdoor temperature and the humidity [39]; vision sensors [55]; sensor networks [56,104]; binary infrared sensors [83]; motion detectors, light sensors, meteorological sensors for the wind and solar radiation data [105]; light and motion sensors [106]; environmental sensors [107]; in-house and city sensors [108]; meteorological stations [46]; smart home sensors, remote monitoring systems, and data and video review systems [102]; temperature and infrared sensors [109]; temperature sensors [110]; inside and outside home sensors [111]; different sensors and effectors [112]; smart systems for controlling the vibration of building structures by means of smart dampers [113]; virtual sensor based on a fisheye video camera [48]; and indoor and outdoor light sensors [114]. In these papers, the reasons for using the Fuzzy C-Means with the sensor devices in smart buildings were mainly related to monitoring and controlling energy management processes [24,39,46,47,…”

“…The performance metrics considered in the scientific papers that use the Fuzzy C-Means integrated with sensor devices in smart buildings were evaluated based on experiments and simulations [46,47,103,[107][108][109]111,114]; Root Mean Square Error (RMSE) [24]; computational cost, user anonymity, mutual authentication, off-line password guessing attacks, impersonation attacks, replay attacks, and the assurance of formal security [99]; Inaccuracy Rate, experiment environment dimension and Root-Mean-Square Error (RMSE), and the dependency of the localization approach on the number of wireless nodes (topology) employed to locate the objects [100]; Accuracy [101,110]; Coefficient of Determination (R 2 ) [49]; energy consumption, Electricity Cost, Peak-to-Average Ratio (PAR) [54]; energy saving percentage in different working scenarios [39]; Standard Error of Mean (SEM), Horizontal Illuminance, Daylight Glare Probability, paper-based Landolt test, Freiburg Visual Acuity Test (FrACT), Electric Lighting Energy Consumption, total number of shading and lighting commands [55]; turbulence intensity, draught rates, operative temperature, Predicted Mean Vote (PMV) and Percentage of People Dissatisfied (PPD) [56]; Identification Rate [83]; Energy Consumption and illumination level [105]; energy savings [106]; Detection Accuracy, Energy Consumption, Memory Consumption, Processing Time Estimation [104]; True Positive, False Positive, True Negative, False Negative, and Accuracy [102]; Accuracy and a comparison with the results presented in related works (based on Ultrasonic, Ultrasonic/RFID, ZigBee, Active RFID, Passive RFID) [112]; Fault Detection Index values for certain fault magnitudes, residual values for individual sensors corresponding to different fault magnitudes [113]; and comfort level [48].…”

Fuzzy Efficient Energy Smart Home Management System for Renewable Energy Resources

“…Microgrids are classified as low-and mid-voltage networks comprising renewable distributed generating units of various kinds, scheduled loads, and storage systems that transmit or connect local loads [15]. In the area of optimum complex systems control, constancy is required to increase smart grids and real-time pricing, and the growth of distributed power generation, as well as electric vehicles and storage systems [9]. The major purpose of the study is to solve the issues faced by new electricity consumers and producers trying to maximize the advantages of modern technology to control their energy [10].…”