A dynamic fuzzy controller to meet thermal comfort by using neural network forecasted parameters as the input

Energies

Volumn 7, Issue 8, 2014, Pages 4727-4756

  Collotta, Mario ^a   Messineo, Antonio ^a   Nicolosi, Giuseppina ^a   Pau, Giovanni ^a  

^a KORE UNIVERSITY OF ENNA   (Italy)

Author keywords

Artificial neural networks; Fuzzy logic controller; HVAC intelligent systems; Thermal comfort

Indexed keywords

AIR CONDITIONING; COMPUTER CIRCUITS; CONTROLLERS; FUZZY INFERENCE; FUZZY NEURAL NETWORKS; INTELLIGENT SYSTEMS; MEMBERSHIP FUNCTIONS; NEURAL NETWORKS; SPACE HEATING; TEMPERATURE CONTROL; TEMPORAL LOGIC; THERMAL COMFORT;

ANALYTICAL CALCULATION; AUTOMATIC REGULATION; EMBEDDING PARAMETERS; FUZZY LOGIC CONTROL; FUZZY LOGIC CONTROLLERS; INDOOR AIR TEMPERATURE; MULTIVARIATE SYSTEMS; NEURAL NETWORK MODEL;

FUZZY LOGIC;

EID: 84906911086     PISSN: None     EISSN: 19961073     Source Type: Journal    
DOI: 10.3390/en7084727     Document Type: Article

References (65)

1. Liang, H.H.; Chen, C.P.; Hwang, R.L.; Shih,W.M.; Lo, S.C.; Liao, H.Y. Satisfaction of occupants toward indoor environment quality of certified green office buildings in Taiwan. Build. Environ. 2014, 72, 232-242.
2. Tho¨rn, A. The sick building syndrome: A diagnostic dilemma. Soc. Sci. Med. 1998, 47, 1307-1312.
3. Kolokotsa, D.; Tsiavos, D.; Stavrakakis, G.S.; Kalaitzakis, K.; Antonidakis, E. Advanced fuzzy logic controllers design and evaluation for buildings' occupants thermal-visual comfort and indoor air quality satisfaction. Energy Build. 2001, 33, 531-543.
4. Kaarlela-Tuomaala, A.; Helenius, R.; Keskinen, E.; Hongisto, V. Effects of acoustic environment on work in private office rooms and open-plan offices-Longitudinal study during relocation. Ergonomics 2009, 52, 1423-1444.
5. Freewan, A.A.Y. Impact of external shading devices on thermal and daylighting performance of offices in hot climate regions. Sol. Energy 2014, 102, 14-30.
6. Kosonen, R.; Tan, F. Assessment of productivity loss in air-conditioned buildings using PMV index. Energy Build. 2004, 36, 987-993.
7. Mahbob, N.S.; Kamaruzzaman, S.N.; Salleh, N.; Sulaiman, R. Correlation and regression studies of indoor environmental quality (IEQ), human productivity, comfort and stress level in office buildings. Adv. Sci. Lett. 2013, 19, 342-345.
8. Wyon, D.P. The effects of indoor air quality on performance and productivity. Indoor Air 2004, 14, 92-101.
9. Fisk, W.J.; Black, D.; Brunner, G. Benefits and costs of improved IEQ in U.S. offices. Indoor Air 2011, 21, 357-367.
10. ISO 7730. Moderate Thermal Environments-Determination of the PMV and PPD Indices and Specification of the Conditions for Thermal Comfort. International Organisation for Standardization, Geneva, Switzerland, 1994.
11. ANSI/ASHRAE Standard 55-2010. Thermal Environmental Conditions for Human Occupancy. American Society of Heating, Refrigerating, and Air-Conditioning Engineers Inc, Atlanta. 2010.
12. Olesen, B.W. International standards for the indoor environment. Indoor Air 2004, 14, 18-26.
13. Yildiz, Y.; Arsan, Z.D. Identification of the building parameters that influence heating and cooling energy loads for apartment buildings in hot-humid climates. Energy 2011, 36, 4287-4296.
14. Liu, W.; Lian, Z.; Zhao, B. A neural network evaluation model for individual thermal comfort. Energy Build. 2007, 39, 1115-1122.
15. Guo, W.Q.; Zhou, M.C. Technologies toward thermal comfort-based and energy-efficient HVAC systems: A review. In Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC 2009), San Antonio, TX, USA, 11-14 October 2009; pp. 3883-3888.
16. Mirinejad, H.; Sadati, S.H.; Ghasemian, M.; Torab, H. Control techniques in heating, ventilating and air conditioning systems. J. Computer Sci. 2008, 4, 777-783.
17. Li, M.; Mu, H.; Li, H. Analysis and assessments of combined cooling, heating and power systems in various operation modes for a building in China, Dalian. Energies 2013, 6, 2446-2467.
18. Sun, Y.N.; Elizondo, M.; Lu, S.; Fuller, J.C. The impact of uncertain physical parameters on HVAC demand response. IEEE Tran. Smart Grid 2014, 5, 916-923.
19. Valancius, R.; Jurelionis, A.; Dorosevas, V. Method for cost-benefit analysis of improved indoor climate conditions and reduced energy consumption in office buildings. Energies 2013, 6, 4591-4606.
20. Marvuglia, A.; Messineo, A.; Nicolosi, G. Coupling a neural network temperature predictor and a fuzzy logic controller to perform thermal comfort regulation in an office building. Build. Environ. 2014, 73, 287-299.
21. Mettam, K.C. In Human Thermal Environment, 2nd ed.; Taylor & Francis: London, UK, 2003.
22. Frontczak, M.; Wargocki, P. Literature survey on how different factors influence human comfort in indoor environments. Build. Environ. 2011, 46, 922-937.
23. Zalejska-Jonsson, A.;Wilhelmsson, M. Impact of perceived indoor environment quality on overall satisfaction in Swedish dwellings. Build. Environ. 2013, 63, 134-144.
24. Collotta, M.; Pau, G.; Scata´, G. A Fuzzy System to reduce Power Consumption in Wireless Sensor Networks: A comparison between WirelessHART and IEEE 802.15.4. In Proceedings of 2014 IEEE International Energy Conference and Exhibition (ENERGYCON), Dubrovnik, Croatia, 13-16 May 2014.
25. Artale, V.; Collotta, M.; Pau, G.; Ricciardello, A. Hexacopter trajectory control using a neural network. In Proceedings of 11th International Conference of Numerical Analysis and Applied Mathematics (ICNAAM), Rhodes, Greece, 21-27 September 2013; pp. 1216-1219.
26. Collotta, M.; Lo Cascio, A.; Pau, G.; Scata´, G. A fuzzy controller to improve CSMA/CA performance in IEEE 802.15.4 industrial wireless sensor networks. In Proceedings of 2013 IEEE 18th Conference on Emerging Technologies & Factory Automation (ETFA), Cagliari, Italy, 10-13 September 2013; pp. 1-4.
27. Gouda, M.M.; Danaher, S.; Underwood, C.P. Quasi-adaptive fuzzy heating control of solar buildings. Build. Environ. 2006, 41, 1881-1891.
28. Homod, R.Z.; Sahari, K.S.M.; Almurib, H.A.F.; Nagi, F.H. RLF and TS fuzzy model identification of indoor thermal comfort based on PMV/PPD. Build. Environ. 2012, 49, 141-153.
29. Fanger, P.O. Thermal Comfort: Analysis and Applications in Environmental Engineering; McGraw-Hill: New York, NY, USA, 1972.
30. Allen, W.H.; Rubaai, A. Fuzzy-neuro Health Monitoring System for HVAC system variable-air-volume unit. In Proceedings of 2013 IEEE Industry Applications Society Annual Meeting, Lake Buena Vista, FL, USA, 6-11 October 2013; pp. 1-8.
31. Du, Z.M.; Fan, B.; Chi, J.L.; Jin, X.Q. Sensor fault detection and its efficiency analysis in air handling unit using the combined neural networks. Energy Build. 2014, 72, 157-166.
32. Marvuglia, A.; Messineo, A. Using recurrent artificial neural networks to forecast household electricity consumption. Energy Procedia 2012, 14, 45-55.
33. Marvuglia, A.; Messineo, A. Monitoring of wind farms' power curves using machine learning techniques. Appl. Energy 2012, 98, 574-583.
34. Moon, J.W.; Chang, J.D.; Kim, S. Determining adaptability performance of artificial neural network-based thermal control logics for envelope conditions in residential buildings. Energies 2013, 6, 3548-3570.
35. Kusiak, A.; Xu, G. Modeling and optimization of HVAC systems using a dynamic neural network. Energy 2012, 42, 241-250.
36. Farhadi, P.; Ziaei, M.; Bayati, M.; Ramezani, E.; Sojoudi, T. Fuzzy control performance on unified power flow controller to increase power system stability. In Proceedings of 2013 Fourth International Conference on the Power Engineering, Energy and Electrical Drives (POWERENG), Istanbul, Turkey, 13-17 May 2013; pp. 1421-1426.
37. Lv, H.L.; Duan, P.Y.; Jia, L. A novel fuzzy controller design based-on PID gains for HVAC systems. In Proceedings of 7th World Congress on Intelligent Control and Automation (WCICA 2008), Chongqing, China, 25-27 June 2008; pp. 736-739.
38. Hassan Khooban, M.; Nazari Maryam Abadi, D.; Alfi, A.; Siahi, M. Optimal Type-2 fuzzy controller for HVAC systems. Automatika 2014, 55, 69-78.
39. Ali, I.M. Developing of a fuzzy logic controller for air conditioning system. AJES 2012, 5, 180-187.
40. Bai, J. Development an adaptive incremental fuzzy PI controller for a HVAC system. Int. J. Comput. Commun. 2013, 8, 654-661.
41. Dash, S.K.; Mohanty, G.; Mohanty, A. Intelligent air conditioning system using fuzzy logic. Int. J. Sci. Eng. Res. 2012, 3, 1-6.
42. Takagi, T.; Sugeno, M. Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst. Man Cybern. 1985, 1, 116-132.
43. Reznik, L. Fuzzy Controllers Handbook: How to Design Them, How They Work; Newnes: Amsterdam, The Netherlands, 1997.
44. Garibaldi, J.M.; John, R.I. Choosing membership functions of linguistic terms. In Proceedings of 12th IEEE International Conference on Fuzzy Systems (FUZZ'03), St. Louis, MO, USA, 25-28 May 2003; pp. 578-583.
45. Preuss, H.P.; Tresp, V. Neuro-Fuzzy. Automatisierungstechnische Praxis 1994, 36, 10-24. (In German)
46. Wag, L.X. A Course on Fuzzy Systems and Control; Prentice Hall: Upper Saddle River, NJ, USA, 1997.
47. Olunloyo, V.O.S.; Ajofoyinbo, A.M.; Ibidapo-Obe, O. On development of fuzzy controller: The case of gaussian and triangular membership functions. J. Signal Inf. Process. 2011, 2, 257-265.
48. PIC24FJ256GB110 Family Data Sheet, Microchip Technology. Available online: http://www.microchip.com (accessed on 20 July 2014).
49. Nørgaard, M.; Ravn, O.; Poulsen, N.K.; Hansen, L.K. Neural Networks for Modelling and Control of Dynamic Systems; Springer-Verlag: London, UK, 2000.
50. Hagan, M.T.; Menhaj, M. Training feedforward networks with the Marquardt algorithm. IEEE Trans. Neural Netw. 1994, 5, 989-993.
51. Takens, F. Detecting strange attractors in turbulence. Springer Lect. Notes Math. 1981, 898, 366-381.
52. Kembe, G.; Fowler, A.C. A correlation function for choosing time delays in phase portrait reconstructions. Phys. Lett. A 1993, 179, 72-80.
53. Ma, H.; Han, C. Selection of embedding dimension and delay time in phase space reconstruction. Front. Electr. Electron. Eng. China 2006, 1, 111-114.
54. Albano, A.M.; Muench, J.; Schwartz, C.; Mees, A.I.; Rapp, P.E. Singular-value decomposition and the Grassberger-Procaccia algorithm. Phys. Rev. A 1988, 38, 3017-3026.
55. Fraser, A.M. Information and entropy in strange attractors. IEEE Trans. Inf. Theory 1989, 35, 245-262.
56. Buzug, T.P. Optimal delay time and embedding dimension for delay-time coordinates by analysis of the global static and local dynamical behavior of strange attractors. Phys. Rev. A 1992, 45, 7073-7084.
57. Lin, J.;Wang, Y.; Huang, Z.; Shen, Z. Selection of proper time-delay in phase space reconstruction of speech signals. Signal Process 1999, 15, 220-225.
58. Kugiumtzis, D. State space reconstruction parameters in the analysis of chaotic time series: The role of the time window length. Physica D 1996, 95, 13-28.
59. Kim, H.S.; Eykholt, R.; Salas, J.D. Nonlinear dynamics, delay times, and embedding windows. Physica D 1999, 127, 48-60.
60. Otani, M.; Jones, A. Automated Embedding and Creep Phenomenon in Chaotic Time Series. Available online: http://users.cs.cf.ac.uk/Antonia.J.Jones/UnpublishedPapers/Creep.pdf (accessed on 20 July 2014).
61. Kim, K.H.; Jeong, J.J.; Lee, S.J.; Moon, S.; Kim, S.W. Modeling of DC electric arc furnace using chaos theory and neural network. In Proceedings of 12th International Conference on Control, Automation and Systems (ICCAS), JeJu Island, Korea, 17-21 October 2012; pp. 1675-1678.
62. Su, L.Y.; Liu, R.H.; Li, F.L.; Li, J.J. Prediction comparative study of complex multivariate systems with AGA-BP. In Proceedings of International Conference on Computer Application and System Modeling, Taiyuan, China, 22-24 October 2010; pp. 50-54.
63. Mizumoto, M.; Tanaka, K. Some properties of fuzzy sets of type 2. Inf. Control 1976, 31, 312-340.
64. Kon, M.A.; Plaskota, L. Information complexity of neural networks. Available online: http://math.bu.edu/people/mkon/nn30.pdf (accessed on 20 July 2014).
65. Directive 2012/27/EU of the European Parliament and of the Council. Available online: http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2012:315:0001:0056:EN:PDF (accessed on 20 July 2014)