Advertisement

The Neural System of Monitoring and Evaluating the Parameters of the Elements of an Intelligent Building

  • Andrzej StachnoEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10672)

Abstract

In the intelligent buildings more and more features are dependent on the proper operation of equipment powered by electricity. Possible damage to these devices is very detrimental to the functioning of the building and must be repaired or replaced immediately. In many cases a failure prevents the building from being used. Possible equipment failures, however, can be predicted from the analysis of how they operate. Monitoring and analysis of power parameters of individual electrical devices allow to distinguish characteristic parameters of each electrical receiver. Any departure from stable working conditions may be recorded by a neuronal monitoring and evaluation system. The mechanism for such an analysis is the implementation of an adaptive prediction algorithm using artificial neural networks. This method allows adaptation of the decision mechanism to the current working conditions of controlled devices. The measured parameters are the measurements of physical quantities that illustrate the operation of the device. For example, for air handling and ventilation units, this is the electricity consumed. The advantage of power analysis is the identification of common faults and corresponding deformations of power supply parameters. Based on the previously prepared pattern, neural networks identify component damage or predict the predicted critical failure time of the component or control subsystem using MWF. Early forecasting of the failure situation contributes significantly to the comfort and security of intelligent building users.

Keywords

Artificial neuron networks FFT Forecasting Successive values of a time series Environmental measurements in an intelligent building 

References

  1. 1.
    Suproniuk, M., Kamiński, P., Pawłowski, M., Kozłowski, R., Pawłowski, M.: An intelligent measurement system for the characterisation of defect centres in semi-insulating materials. Electr. Rev. 86(12), 247–252 (2010)Google Scholar
  2. 2.
    Peitgen, H.O., Jurgens, H., Saupe, D.: Introduction to Fractals and Chaos. PWN, Warsaw (2002)zbMATHGoogle Scholar
  3. 3.
    Technical Analysis of the Financial Markets. WIG Press (1999)Google Scholar
  4. 4.
    Jabłoński, A.: Intelligent buildings as distributed information systems. CASYS: Int. J. Comput. Anticip. Syst. 21, 385–394 (2008)Google Scholar
  5. 5.
    Stachno, A., Jablonski, A.: Hybrid method for forecasting next values of time series for intelligent building control. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds.) EUROCAST 2015. LNCS, vol. 9520, pp. 822–829. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-27340-2_101. ISSN 0302-9743CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Department of Control Systems and MechatronicsWroclaw University of Science and TechnologyWroclawPoland

Personalised recommendations