IoT Operating System Based Fuzzy Inference System for Home Energy Management System in Smart Buildings Article Swipe
Qurat-ul Ain
,
Sohail Iqbal
,
Safdar Abbas Khan
,
Asad Waqar Malik
,
Iftikhar Ahmad
,
Nadeem Javaid
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.3390/s18092802
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/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%.
Related Topics
Concepts
Thermostat
Energy consumption
Computer science
Fuzzy logic
Thermal comfort
Energy management
Real-time computing
Air conditioning
Building automation
Energy management system
Energy (signal processing)
Automotive engineering
Simulation
Embedded system
Engineering
Artificial intelligence
Electrical engineering
Mechanical engineering
Physics
Statistics
Thermodynamics
Mathematics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s18092802
- https://www.mdpi.com/1424-8220/18/9/2802/pdf?version=1535506656
- OA Status
- gold
- Cited By
- 86
- References
- 47
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2889024658
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2889024658Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s18092802Digital Object Identifier
- Title
-
IoT Operating System Based Fuzzy Inference System for Home Energy Management System in Smart BuildingsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2018Year of publication
- Publication date
-
2018-08-25Full publication date if available
- Authors
-
Qurat-ul Ain, Sohail Iqbal, Safdar Abbas Khan, Asad Waqar Malik, Iftikhar Ahmad, Nadeem JavaidList of authors in order
- Landing page
-
https://doi.org/10.3390/s18092802Publisher landing page
- PDF URL
-
https://www.mdpi.com/1424-8220/18/9/2802/pdf?version=1535506656Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1424-8220/18/9/2802/pdf?version=1535506656Direct OA link when available
- Concepts
-
Thermostat, Energy consumption, Computer science, Fuzzy logic, Thermal comfort, Energy management, Real-time computing, Air conditioning, Building automation, Energy management system, Energy (signal processing), Automotive engineering, Simulation, Embedded system, Engineering, Artificial intelligence, Electrical engineering, Mechanical engineering, Physics, Statistics, Thermodynamics, MathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
86Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 6, 2024: 7, 2023: 12, 2022: 19, 2021: 15Per-year citation counts (last 5 years)
- References (count)
-
47Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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