Outdoor Temperature Estimation Using ANFIS for Soft Sensors

Journal: Journal of Autonomous Intelligence DOI: 10.32629/jai.v2i3.58

Zahra Pezeshki1, Sayyed Majid Mazinani2, Elnaz Omidvar3

1. Faculty of Electrical and Robotic Engineering, Shahrood University of Technology, Researcher
2. Electrical Department, Faculty of Engineering, Imam Reza International University, Associate Professor
3. Department of Artificial Intelligence, Khayyam Higher Education University, Student


In recent years, several studies using smart methods and soft computing in the field of HVAC systems have been provided. In this paper, we propose a framework which will strengthen the benefits of the Fuzzy Logic (FL) and Neural Fuzzy (NF) systems to estimate outdoor temperature. In this regard, Adaptive Neuro Fuzzy Inference System (ANFIS) is used in effective combination of strategic information for estimating the outdoor temperature of the building. A novel versatile calculation focused around ANFIS is proposed to adjust logical progressions and to weaken the questionable aggravation of estimation information from multisensory. Due to ANFIS accuracy in specialized predictions, it is an effective device to manage vulnerabilities of each experiential framework. The NF system can concentrate on measurable properties of the samples throughout the preparation sessions. Reproduction results demonstrate that the calculation can successfully alter the framework to adjust context oriented progressions and has solid combination capacity in opposing questionable data. This sagacious estimator is actualized utilizing Matlab and the exhibitions are explored. The aim of this study is to improve the overall performance of HVAC systems in terms of energy efficiency and thermal comfort in the building.


Soft Sensor; ANFIS; Neuro Fuzzy; Outdoor Temperature; Soft Computing


[1]Liu J. On-line soft sensor for polyethylene process with multiple production grades. Control Engineering Practice 2007; 15(7): 769-778.
[2]Kocijan J, Prikryl J. Soft sensor for faulty measurements detection and reconstruction in urban traffic. Proceedings of the IEEE Mediterranean Electro technical Conference 2010; 172-177. http://dx.doi.org/ 10.1109/MELCON.2010.5476311
[3]Sliškovic D, Grbic R, Hocenski Ž. Methods for plant data-based process modeling in soft-sensor development. ATKAFF 2011; 52(4): 306-318.
[4]Lin B, Recke B, Knudsen JKH, et al. A systematic approach for soft sensor development. Computers & Chemical Engineering 2007; 31(5-6): 419-425.
[5]Omidvar E, Mazinani SM, Pezeshki Z. Description and comparison the performance of the fuzzy, neural fuzzy and adaptive neuro fuzzy controller thermal sensors in air conditioning systems. Proceedings of the National Conference on Electrical Engineering, 9th Symposium on Advances in Science & Technology, Khavaran University, Mashhad, Iran (in Persian), 2014. http://dx.doi.org/10.13140/RG.2.2.32525.23527
[6]Jassar S, Liao Z, Zhao L. A recurrent neuro-fuzzy system and its application in inferential sensing. Applied Soft Computing 2011; 11(3): 2935-2945.
[7]Jassar S, Liao Z, Zhao L. Adaptive neuro-fuzzy based inferential sensor model for estimating the average air temperature in space heating systems. Building and Environment 2009; 44(8): 1609-1616.
[8]Jassar S, Behan T, Zhao L, et al. The comparison of neural network and hybrid neuro-fuzzy based inferential sensor models for space heating systems. Proceedings of the 2009 IEEE International Conference on Systems, Man, and Cybernetics, San Antonio, TX, USA, 4299-4303.
[9]Huang L, Liao Z, Zhao L. Physical-rules-based adaptive neuro-fuzzy inferential sensor model for predicting the indoor temperature in heating systems. International Journal of Distributed Sensor Networks 2012; 8(6): 1-10.
[10]Ali AH, Shamshirband S, Annuar NB, et al. DFCL: Dynamic fuzzy logic controller for intrusion detection. Facta Universitatis Series: Mech. Eng. 2014; 12(2): 183-193.
[11]Shamshirband S, Anuar NB, Mat Kiah ML, et al. Co-FAIS: Cooperative fuzzy artificial immune system for detecting intrusion in wireless sensor networks. J. Netw. Comput. Appl. 2014; 42: 102-117.
[12]Shamshirband S, Hessam S, Javidnia H, et al. Tuberculosis disease diagnosis using artificial immune recognition system. Int. J. Med. Sci. 2014; 11(5): 508-514.
[13]Shamshirband S, Amini A, Anuar NB, et al. D-FICCA: A density-based fuzzy imperialist competitive clustering algorithm for intrusion detection in wireless sensor networks. Measurement 2014; 55: 212-226.
[14]Shamshirband S, Anuar NB, Mat Kiah ML, et al. An appraisal and design of a multi-agent system based cooperative wireless intrusion detection computational intelligence technique. Eng. Appl. Artif. Intel. 2013; 26(9): 2105-2127.
[15]Shamshirband S, Patel A, Anuar NB, et al. Cooperative game theoretic approach using fuzzy Qlearning for detecting and preventing intrusions in wireless sensor networks. Eng. Appl. Artif. Intel. 2014; 32: 228-241.
[16]Khan AN, Mat Kiah ML, Madani SA, et al. Incremental proxy re-encryption scheme for mobile cloud computing environment. J. Supercomput. 2014; 68(2): 624-651.
[17]Cregan V, Lee WT, Clune L. A soft sensor for the Bayer process. Journal of Mathematics in Industry 2017; 7(7):1-7.
[18]Lin M, Gutierrez NG, Xu S. Soft sensors form a network. Nature Electronics 2019; 2: 327-328. 
[19]Pezeshki Z, Mazinani SM. Comparison of artificial neural networks, fuzzy logic and neuro fuzzy for predicting optimization of building thermal information. In 14th Iranian Conference on Fuzzy Systems. Sahand University of Technology,Tabriz, Iran, 2014; pp. 629-639. https://doi.org/10.13140/rg.2.2.21883. 41766 (in Persian).
[20]Pezeshki Z, Mazinani SM. Comparison of artificial neural networks, fuzzy logic and neuro fuzzy for predicting optimization of building thermal consumption: a survey. Artificial Intelligence Review 2019; 52(1): 495-525.
[21]Mojedifar S, Ranjbar H, Nezamabadi-pour H. Adaptive neuro-fuzzy inference system application for hydrothermal alteration mapping using ASTER data. Journal of Mining & Environment 2013; 4(2): 83-96.
[22]Jassar S, Liao Z, Zhao L. Data Quality in ANFIS Based Soft Sensors. In: Ao SI., Rieger B., Amouzegar M. (eds) Machine Learning and Systems Engineering. Lecture Notes in Electrical Engineering 2010; vol 68. Springer, Dordrecht.
[23]Lahiri SK. Multivariable Predictive Control: Applications in Industry. 1st Edition, Published by John Wiley & Sons Ltd., US, 2017.
[24]Kadlec P, Gabrys B, Strandt S. Data-driven soft sensors in the processindustry. Computers & Chemical Engineering 2009; 33(4):795-814.
[25]Bakirov R, Gabrys B, Fay D. Multiple adaptive mechanisms for data-driven soft sensors. Computers & Chemical Engineering 2017; 96: 42-54.
[26]Fortuna L, Graziani S, Rizzo A, et al. Soft sensors for monitoring and control of industrial processes. Advances in Industrial Control, Springer 2006.
[27]Ge Z, Huang B, Song Z. Nonlinear semi-supervised principal component regression for soft sensor modeling and its mixture form. J. Chemom 2014; 28(11):793-804.
[28]Graziani S, Xibilia MG. Springer Nature Switzerland AG, W. Pedrycz and S.-M. Chen (eds.), Development and Analysis of Deep Learning Architectures, Studies in Computational Intelligence 867, 2020. https://doi.org/10.1007/978-3-030-31764-5_2.
[29]Han J-H, Lee I-B. Development of a scalable infrastructure model for planning electricity generation and CO2 mitigation strategies under mandated reduction of GHG emission. Applied Energy 2011; 88(12): 5056-5068.
[30]Tian H, Shuai M, Li K, et al. An incremental learning ensemble strategy for industrial process soft sensors. Complexity 2019; 2019:1-12. Article ID: 5353296, https://doi.org/10.1155/2019/5353296.
[31]Tissier JF, Cornet J. New soft sensors for distribution transformer monitoring. In 24th International Conference & Exhibition on Electricity Distribution (CIRED), Open Access Proc. J. 2017; pp. 1-5.
[32]Funatsu K. Process control and soft sensors. In Applied Chemoinformatics (eds T. Engel and J. Gasteiger), 2019. doi:10.1002/9783527806539.ch13.
[33]Miao A, Li P, Ye L. Locality preserving based data regression and its application for soft sensor modelling. Can. J. Chem. Eng. 2016; 94: 1977-1986. doi:10.1002/cjce.22568.
[34]Najar S, Etien E. Soft sensor for distribution transformers: Thermal and electrical models. In 23rd International Conference on Electricity Distribution. Lyon, 2015; pp. 1-5.
[35]Shao W, Tian X. A soft sensor method based on Integrated PCA. Proceedings of the 10th World Congress on Intelligent Control and Automation, Beijing, 2012; pp. 4258-4263. doi: 10.1109/WCICA.2012.6359194.
[36]Shi X, Xiong W. Approximate linear dependence criteria with active learning for smart soft sensor design. Chemometrics and Intelligent Laboratory Systems 2018; 180:88-95.
[37]Teng L, Pan K, Nemitz MP, et al. Soft radio-frequency identification sensors: Wireless long-range strain sensors using radio-frequency identification. Soft Robotics 2019; 6(1):82-94.
[38]Wang Y, Chen X. On temperature soft sensor model of rotary kiln burning zone based on RS-LSSVM. Proceedings of the 36th Chinese Control Conference, Dalian, China, 2017; pp. 9643-9646.
[39]Yan W, Tang D, Lin Y. A data-driven soft sensor modeling method based on deep learning and its application. In IEEE Transactions on Industrial Electronics 2017; 64(5): 4237-4245. doi: 10.1109/TIE.2016.2622668.
[40]Jang JSR. ANFIS: Adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics 1993; 23: 665–685.

Copyright © 2019 Zahra Pezeshki, Sayyed Majid Mazinani, Elnaz Omidvar

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