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Comparison of artificial neural networks, fuzzy logic and neuro fuzzy for predicting optimization of building thermal consumption: a survey

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Abstract

Data Mining (DM) is a useful technique to discover useful patterns which lead to large searches. This method offers a reliable treatment of all developmental phases from problem and data understanding through data preprocessing to deployment of the results. DM plays an important role in energy efficiency. The construction industry has numerous sources information to compare and turn them into beneficial information. Artificial neural networks (ANN), fuzzy logic (FL) and neuro fuzzy (NF) are used techniques. Although the ANN and FL have many advantages, they also have certain defects. NF enjoys the advantages of both ANN and FL. In this paper, by comparing these techniques present in articles from 2009 to 2017, we have introduced four advantages for NF technique and indicated that the second advantage has been paid less attention other ones. The results reveal that the NF method is more successful than FL and ANN for predicting the thermal efficiency of buildings. However, NF with a learning phase proves to be computationally heavy and time-consuming, especially when the number of rules, the antecedents and the model delays are high. As a result, the proposed method, using nonlinear neural Model Predictive Controllers, is the best answer to thermal control strategies.

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References

  • Ahmad S, Khan L (2017) Performance analysis of conjugate gradient algorithms applied to the neuro-fuzzy feedback linearization-based adaptive control paradigm for multiple HVDC Links in AC/DC power system. Energies 10(819):1–23

    Google Scholar 

  • Ahmad A, Hassan M, Abdullah M, Rahman H, Hussin F, Abdullah H, Saidur R (2014) A review on applications of ANN and SVM for building electrical energy consumption forecasting. Renew Sustain Energy Rev 33:102–109

    Article  Google Scholar 

  • Ak V (1993) An approach for the design of the energy efficient building and the settlement. Ph.D. thesis. Natural Sciences Institute, Istanbul Technical University, Istanbul

  • Alasha’ary H, Moghtaderi B, Page A, Sugo H (2009) A neuro–fuzzy model for prediction of the indoor temperature in typical Australian residential buildings. Energy Buildings 41:703–710

    Article  Google Scholar 

  • Ali MM, Khan MA, Shafi M, Abdul M, Khan O, Farooky MAA, Azam S, Khasim S, Ahmed SWE (2014) Heat ventilation & air-conditioning system with self-tuning fuzzy PI controller. Int J Mod Eng Res (IJMER) 4(9):24–35

    Google Scholar 

  • Allouhi A, El Fouih Y, Kousksou T, Jamil A, Zeraouli Y, Mourad Y (2015) Energy consumption and efficiency in buildings: current status and future trends. J Clean Prod 109:118–130

    Article  Google Scholar 

  • Atia DM, El-madany HT (2016) Analysis and design of greenhouse temperature control using adaptive neuro-fuzzy inference system. J Electr Syst Inf Technol. 5 Dec 2016

  • Attia S (2010) Building performance simulation tools: selection criteria and user survey. Universit Catholique de Louvain, Louvain La Neuve

    Google Scholar 

  • Bahar YN, Pere C, Landrieu J, Nicolle C (2013) A thermal simulation tool for building and its interoperability through the building information modelling (BIM) platform. Buildings 3(2):380–398

    Article  Google Scholar 

  • Czogaa E, Leski J (2000) Fuzzy and neuro-fuzzy intelligent systems, Studies in fuzziness and soft computing, vol 47. Springer. https://doi.org/10.1007/978-3-7908-1853-6

  • da Silva IN, Spatti DH, Flauzino RA, Liboni LHB (2017) Artificial neural network architectures and training processes. In: Artificial neural networks. Springer, Berlin, pp 21–28. https://doi.org/10.1007/978-3-319-43162-8_2

  • Deb C, Eang LS, Yang J, Santamouris M (2015) Forecasting energy consumption of institutional buildings in Singapore. Procedia Engineering 121:1734–1740

    Article  Google Scholar 

  • Dehestani D, Su S, Nguyen H, Guo Y (2013) Robust fault tolerant application for HVAC system based on combination of online SVM and ANN black box model. In: 2013 European Control Conference (ECC), Zurich, pp 2976–2981

  • Ekici BB, Aksoy UT (2009) Prediction of building energy consumption by using artificial neural networks. Adv Eng Softw 40:356–362

    Article  MATH  Google Scholar 

  • Fayyad U, Piatetsky-Shapiro G, Smyth P (1996) From data mining to knowledge discovery in databases. American Association for Artificial Intelligence 17(3):37–54. https://doi.org/10.1609/aimag.v17i3.1230

    Google Scholar 

  • Ferreira PM, Ruano AE, Silva S, Conceicao EZE (2012) Neural networks based predictive control for thermal comfort and energy savings in buildings. Energy Build 55:238–251

    Article  Google Scholar 

  • Fletcher R (1987) Practical methods of optimization, 2nd edn. Wiley, Chichester, pp 80–94

    MATH  Google Scholar 

  • Ghadi YY, Rasul MMG, Khan MKK (2014) Recent developments of advanced fuzzy logic controllers used in smart buildings in subtropical climate. Energy Procedia 61:1021–1024

    Article  Google Scholar 

  • Graupe D (1997) Chapter 1–12. In: Chen W, Mlynski DA (eds) Principles of artificial neural networks. Advanced Series in circuits and systems, vol 3, 1st edn. World Scientific, p. 4e189

  • Grieu S, Faugeroux O, Traore A, Claudet B, Bodnar J-L (2011) Artificial intelligence tools and inverse methods for estimating the thermal diffusivity of the building materials. Energy Build 43(2–3):543–554

    Article  Google Scholar 

  • Henken J, Biswas MAR (2015) Validation of neural network model for residential energy consumption. In: Proceedings of the 2015 ASEE Gulf-Southwest annual conference. The University of Texas, San Antonio

  • Hosseini MS, Zekri M (2012) Review of medical image classification using the adaptive neuro-fuzzy inference system. Journal of Medical Signals and Sensors 2(1):49–60

    Google Scholar 

  • Kotsopoulos SD, Casalegno F, Cuenin A (2013) Personalizing thermal comfort in a prototype indoor space. In: SIMUL 2013: the fifth international conference on advances in system simulation, IARIA

  • Kumar R, Aggarwal R, Sharma J (2013) Energy analysis of a building using artificial neural network: a review. Energy Build 65:352–358

    Article  Google Scholar 

  • Kumari N, Sunita S (2013) Comparison of ANNs, fuzzy logic and neuro-fuzzy integrated approach for diagnosis of coronary heart disease: a survey. IJCSMC 2(6):216–224

    Google Scholar 

  • Lindelof D, Afshari H, Alisafaee M, Biswas J, Caban M, Mocellin X, Viaene J (2015) Field tests of an adaptive, model-predictive heating controller for residential buildings. Energy Build 99:292–302

    Article  Google Scholar 

  • Lu J-T, Chang Y-C, Ho C-Y (2015) The optimization of chiller loading by adaptive neuro-fuzzy inference system and genetic algorithms. Math Probl Eng 2015, 1–10, Article ID 306401. http://dx.doi.org/10.1155/2015/306401

  • Maile T, Fischer M, Bazjanac V (2007) Building energy performance simulation tools a life-cycle and interoperable perspective. Stanford University, Stanford

    Google Scholar 

  • Mlakić D, Nikolovski S, Vucinic D (2016) Standalone application using JAVA and ANFIS for predicting electric energy consumption based on forecasted temperature. Annals of DAAAM & Proceedings 27:671–677

    Article  Google Scholar 

  • Moon JW, Jung SK, Kim Y, Han S-H (2011) Comparative study of artificial intelligence-based building thermal control methods—application of fuzzy, adaptive neuro-fuzzy inference system, and artificial neural network. Appl Therm Eng 31:2422–2429

    Article  Google Scholar 

  • Moon JW, Lee J-H, Yoon Y, Kim S (2014) Determining optimum control of double skin envelope for indoor thermal environment based on artificial neural network. Energy Build 69:175–183

    Article  Google Scholar 

  • Naji S, Shamshirband S, Basser H, Keivani A, Alengarama UJ, Jumaat MZ, Petkovi D (2016) Application of adaptive neuro fuzzy methodology for estimating building energy consumption. Renew Sustain Energy Rev 53:1520–1528

    Article  Google Scholar 

  • Nauck DD, Nürnberger A (2013) Neuro-fuzzy Systems: a short historical review. In: Moewes C, Nürnberger A (eds) Computational intelligence in intelligent data analysis. Studies in computational intelligence, vol 445. Springer, Berlin

    Google Scholar 

  • Nejat P, Jomehzadeh F, Taheri MM, Gohari M, Majid MZA (2015) A global review of energy consumption, CO2 emissions and policy in the residential sector (with an overview of the top ten CO2 emitting countries). Renew Sustain Energy Rev 43:843–862. https://doi.org/10.1016/j.rser.2014.11.066

    Article  Google Scholar 

  • Nürnberger A (2001) A hierarchical recurrent neuro-fuzzy system. In: Proceedings of joint 9th IFSA world congress and 20th NAFIPS international conference. IEEE, pp 1407–1412

  • Oztemel E (2003) Artificial neural networks. Papatya Publishing, Istanbul

    Google Scholar 

  • Parsons KC (2003) Human thermal environments, 2nd edn. Taylor & Francis, London

    Google Scholar 

  • Perera DWU, Pfeiffer CF, Skeie N-O (2014) Control of temperature and energy consumption in buildings—a review. International Journal of Energy and Environment 5(4):471–484

    Google Scholar 

  • Pérez-Lombard L, Ortiz J, Pout C (2008) A review on buildings energy consumption information. Energy Build 40:394–398. https://doi.org/10.1016/j.enbuild.2007.03.007

    Article  Google Scholar 

  • Pezeshki Z, Ivari SAS (2018) Applications of BIM: a brief review and future outline. Arch Computat Methods Eng. 25(2):273–312. https://doi.org/10.1007/s11831-016-9204-1

    Article  Google Scholar 

  • Powell MJ (1977) Restart procedures for the conjugate gradient method. Math Program 12:241–254

    Article  MathSciNet  MATH  Google Scholar 

  • Shaikh PH, Nor NBM, Nallagownden P, Elamvazuthi I (2013) Building energy management through a distributed fuzzy inference system. International Journal of Engineering and Technology (IJET) 5(4):3236–3242

    Google Scholar 

  • Shamshirband S, Petkovi D, Enayatifa R, Abdullah AH, Markovi D, Lee M, Ahmad R (2015) Heat load prediction in district heating systems with adaptive neuro-fuzzy method. Renew Sustain Energy Rev 48:760–767

    Article  Google Scholar 

  • Shewchuk JR (1994) An introduction to the conjugate gradient method without the agonizing pain. Carnegie Mellon University, Pittsburgh

    Google Scholar 

  • Shi L, Sun Z, Li H, Liu W (2007) Research on diagnosing coronary heart disease using fuzzy adaptive resonance theory mapping neural networks. 2007 IEEE international conference on control and automation, Guangzhou, pp 1126–1128

  • Singhala P, Shah DN, Patel B (2014) Temperature control using fuzzy logic. Int J Instrum Control Syst (IJICS) 4(1):1–10

    Google Scholar 

  • Soares A, Gomes Á, Antunes CH (2017) An evolutionary algorithm for the optimization of residential energy resources. In: Bertsch V, Fichtner W, Heuveline V, Leibfried T (eds) Advances in energy system optimization. Trends in mathematics. Birkhäuser, Cham. https://doi.org/10.1007/978-3-319-51795-7_1

    Google Scholar 

  • Soyguder S, Alli H (2009) An expert system for the humidity and temperature control in HVAC systems using ANFIS and optimization with fuzzy modelling approach. Energy Build 41:814–822

    Article  Google Scholar 

  • Thai TN (2013) Estimate thermo-physical parameters from characterization of the building materials by using artificial intelligence. In: 27th International conference on advanced information networking and applications workshops, Barcelona, pp 329-334

  • Turhan C, Simani S, Zajic I, Akkurt GG (2017) Performance analysis of data-driven and model-based control strategies applied to a thermal unit model. Energies 10(67):1–20

    Google Scholar 

  • U.S. Energy Information Administration (2013) Annual energy outlook 2013 with Projectionto 2040, April 2013

  • van Hoof J (2008) Forty years of Fanger’s model of thermal comfort: comfort for all? Indoor Air 18(3):182–201

    Article  Google Scholar 

  • Wang LX, Mendel JM (1992) Back-propagation fuzzy system as nonlinear dynamic system identification. In: Proceedings of IEEE international conference on fuzzy systems, pp 1409–1418

  • Welle B, Haymaker J, Rogers Z (2011) ThermalOpt: a methodology for automated BIM-based multidisciplinary thermal simulation for use in optimization environments. Build Simul 4(4):293–313

    Article  Google Scholar 

  • Yao Y, Lian ZW, Hou ZJ, Liu W (2006) An innovative air-conditioning load forecasting model based on RBF neural network and combined residual error correction. Int J Refrig 29:528–538

    Article  Google Scholar 

  • Yeh J-P, Yang R-P (2014) Application of the adaptive neuro-fuzzy inference system for optimal design of reinforced concrete beams. J Intell Learn Syst Appl 6:162–175

    Google Scholar 

  • Yu Z, Fung BCM, Haghighat F (2013) Extracting knowledge from building-related data—a data mining framework. Build Simul 6(2):207222

    Article  Google Scholar 

  • Zahedi G, Azizi S, Bahadori A, Elkamel A, Sharifah R, Alwi W (2013) Electricity demand estimation using an adaptive neuro-fuzzy network: a case study from the Ontario province e Canada. Energy 49:323–328

    Article  Google Scholar 

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Acknowledgements

The authors wish to express sincere gratitude to the anonymous reviewers for their constructive comments and helpful suggestions, which lead to substantial improvements of this paper.

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Authors and Affiliations

  1. Faculty of Electrical and Robotic Engineering, Shahrood University of Technology, Shahrud, Iran

    Zahra Pezeshki

  2. Electrical Department, Faculty of Engineering, Imam Reza International University, Mashhad, Iran

    Sayyed Majid Mazinani

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  1. Zahra Pezeshki
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Correspondence to Zahra Pezeshki.

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Pezeshki, Z., Mazinani, S.M. Comparison of artificial neural networks, fuzzy logic and neuro fuzzy for predicting optimization of building thermal consumption: a survey. Artif Intell Rev 52, 495–525 (2019). https://doi.org/10.1007/s10462-018-9630-6

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