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Experimental testing of a random neural network smart controller using a single zone test chamber

Experimental testing of a random neural network smart controller using a single zone test chamber

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Monitoring and analysis of energy use and indoor environmental conditions is an urgent need in large buildings to respond to changing conditions in an efficient manner. Correct estimation of occupancy will further improve energy performance. In this work, a smart controller for maintaining a comfortable environment using multiple random neural networks (RNNs) has been developed. The implementation of RNN-based controller is demonstrated to be more efficient on hardware and requires less memory compared to both artificial neural networks and model predictive controllers. This controller estimates the number of room occupants by using the information from wireless sensor nodes placed in the Heating, Ventilation and Air Conditioning (HVAC) duct and the room. For an occupied room, the controller can switch between thermal comfort mode (based on predicted mean vote set points) and user defined mode (i.e. occupant defined set points for heating/cooling/ventilation). Furthermore, the hybrid particle swarm optimisation with sequential quadratic programming training algorithms are used (for the first time to the best of the authors' knowledge) for training the RNN and results show that this algorithm outperforms the widely used gradient descent algorithm for RNN. The results show that occupancy estimation by smart controller is 83.08% accurate.


    1. 1)
      • 26. Fanger, P.: ‘Thermal comfort: analysis and applications in environmental engineering’, 1972.
    2. 2)
    3. 3)
    4. 4)
    5. 5)
    6. 6)
    7. 7)
    8. 8)
    9. 9)
    10. 10)
    11. 11)
      • 46. Gibson, D., MacGregor, C.: ‘A novel solid state non-dispersive infrared CO2 gas sensor compatible with wireless and portable deployment’, Sensors. 2013, 2013, 13, (6), pp. 70797103,
    12. 12)
      • 8. Liang, J., Du, R.: ‘Thermal comfort control based on neural network for HVAC application’. Proc. of 2005 IEEE Conf. on Control Applications, 2005, CCA 2005, August 2005, pp. 819824.
    13. 13)
    14. 14)
    15. 15)
      • 35. Atalay, V.: ‘Learning by optimization in random neural networks’. Proc. of the 13th Int. Symp. on Computer and Information Sciences, Antalya, Turkey, 1998, pp. 143148.
    16. 16)
      • 16. Reinisch, C., Kofler, M.J., Iglesias, F., et al: ‘Thinkhome energy efficiency in future smart homes’, EURASIP J. Embed. Syst., 2011, 1.
    17. 17)
    18. 18)
    19. 19)
    20. 20)
      • 18. Lu, J., Sookoor, T., Srinivasan, V., et al: ‘The smart thermostat: using occupancy sensors to save energy in homes’. Proc. of the 8th ACM Conf. on Embedded Networked Sensor Systems, November 2010, pp. 211224.
    21. 21)
      • 24. Bhattacharya, S., Sridevi, S., Pitchiah, R.: ‘Indoor air quality monitoring using wireless sensor network’. Sixth Int. Conf. on Sensing Technology (ICST), 2012, December 2012, pp. 422427.
    22. 22)
      • 39. Timotheou, S.: ‘Nonnegative least squares learning for the random neural network’. Artificial Neural Networks-ICANN 2008, Springer, 2008, pp. 195204.
    23. 23)
    24. 24)
    25. 25)
      • 31. Abdelbaki, H., Gelenbe, E., El-Khamy, S.E.: ‘Analog hardware implementation of the random neural network model’. Proc. of the IEEE-INNS-ENNS Int. Joint Conf. on Neural Networks, 2000, IJCNN 2000, 2010, vol. 4, pp. 197201.
    26. 26)
    27. 27)
      • 19. Nguyen, T.A., Aiello, M.: ‘Beyond indoor presence monitoring with simple sensors’. PECCS, 2012, pp. 514.
    28. 28)
      • 5. Oldewurtel, F., Parisio, A., Jones, C.N., et al: ‘Energy efficient building climate control using stochastic model predictive control and weather predictions’. American Control Conf. (ACC), June 2010, pp. 51005105.
    29. 29)
      • 40. Hubert, C.: ‘Pattern completion with the random neural network using the rprop learning algorithm’. IEEE Conf. Proc. Systems, Man and Cybernetics, 1993, ‘Int. Conf. on Systems Engineering in the Service of Humans’, 1993, pp. 613617.
    30. 30)
    31. 31)
      • 22. Agarwal, Y., Balaji, B., Gupta, R., et al: ‘Occupancy-driven energy management for smart building automation’. Proc. of the Second ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building, November 2010, pp. 16.
    32. 32)
      • 45. Ebadat, A., Bottegal, G., Varagnolo, D., et al: ‘Estimation of building occupancy levels through environmental signals deconvolution’. Proc. of the Fifth ACM Workshop on Embedded Systems for Energy-Efficient Buildings, 2013, pp. 18.
    33. 33)
      • 17. Gao, G., Whitehouse, K.: ‘The self-programming thermostat: optimizing setback schedules based on home occupancy patterns’. Proc. of the First ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings, November 2009, pp. 6772.
    34. 34)
      • 4. Javed, A., Larijani, H., Ahmadinia, A., et al: ‘Comparison of the robustness of RNN, MPC and ANN controller for residential heating system’. IEEE Fourth Int. Conf. on Big Data and Cloud Computing (BdCloud), 2014, December 2014, pp. 604611.
    35. 35)
    36. 36)
    37. 37)
    38. 38)
    39. 39)
    40. 40)
      • 14. Liao, C., Barooah, P.: ‘An integrated approach to occupancy modeling and estimation in commercial buildings’. American Control Conf. (ACC), June 2010, pp. 31303135.
    41. 41)
      • 21. Erickson, V.L., Carreira-Perpiñán, M.Á., Cerpa, A.E.: ‘OBSERVE: occupancy-based system for efficient reduction of HVAC energy’. 10th Int. Conf. on Information Processing in Sensor Networks (IPSN), 2011, April 2011, pp. 258269.
    42. 42)
    43. 43)
      • 3. WBCSD: ‘Transforming the market: energy efficiency in buildings’. Survey report, The World Business Council for Sustainable Development, 2009.
    44. 44)
      • 20. Erickson, V.L., Lin, Y., Kamthe, A., et al: ‘Energy efficient building environment control strategies using real-time occupancy measurements’. Proc. of the First ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings, November 2009, pp. 1924.
    45. 45)
    46. 46)

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