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

• Published in: Sensors (Basel, Switzerland) Vol. 18; no. 9; p. 2802
• Main Authors: Iqbal, Sohail, Khan, Safdar Abbas, Malik, Asad Waqar, Ahmad, Iftikhar, Javaid, Nadeem
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 25-08-2018; MDPI
• Subjects: Air conditioners; Air conditioning; Combinatorial analysis; Computer engineering; Computer memory; demand-side management; Energy conservation; Energy consumption; Energy management; Energy management systems; Fuzzy logic; Fuzzy systems; Game theory; home energy management system; Household appliances; Houses; Humidity; HVAC; Inference; International conferences; Internet of Things; Linear programming; Operating systems; Order parameters; Residential buildings; Residential energy; Scheduling; Sensors; Smart buildings; Smart sensors; Software; time-of-use; user comfort; Ventilation; Weather forecasting; Canada; Pakistan; Islamabad Pakistan; United States > US; time-of-use; home energy management system; demand-side management; fuzzy logic; user comfort
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s18092802

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%.