Abstract
Energy savings with building’s automation is related to the functionality of the building’s automation system, equipment automation level, control algorithms and user-selected operating parameters. It is difficult and time-consuming process to customize each of these algorithms to a level, which would ensure the rational use of energy resources. To increase the adjusting quality that controls building’s engineering system it is necessary to implement new advanced methods, technologies and control algorithms within building’s automation system. This work presents the method of creating self-learning system, which is capable of detecting negative influences of different control algorithms. This self-learning system also provides error detection in engineering systems of the building. The system was designed in Matlab and Simulink programming environments. The article also describes the architecture of designing a complex wireless network system with real time (online) connection, including automatics and building’s engineering systems. This complex system is also capable of collecting data to perform an effective intellectual analysis.
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References
B. Chai, W. Tushar, N. U. Hassan, C. Yuen, Z. Yang, Managing energy consumption in buildings through offline and online control of HVAC systems, in 2016 IEEE Region 10 Conference (TENCON)—Proceedings of the International Conference, ISSN 2159-3450, pp. 3368–3373 (2016)
P. Lynggaard, Artificial intelligence and internet of things in a “smart home” context: a distributed system architecture. Ph.D. Thesis, Press/Media, 268 p. (2014)
A. Badlani, S. Bhanot, Smart home system design based on artificial neural networks. ISSN: 2078-0958 (Print); 2078-0966 (Online), in Proceedings of the World Congress on Engineering and Computer Science 2011, vol. I WCECS, pp. 106–111. October 19–21, San Francisco, USA (2011)
K. Sandeep, A.Q. Mohammed, Application of AI in home automation. Int. J. Eng. Technol. 4(6), pp. 803–807 (0975-4024) (2012)
R. Gorzenski, A. Gorka, E. Szczechowiak, Guidelines for the control algorithm of heating installation in LARS low-energy system. Technical Transactions Civil Engineering, R.109, z. -B, pp. 139–148 (2012)
N. Patil, K. Anand, Warad: IOT and Raspberry PI based environmental monitoring application. Int. J. Electr. Electron. Comput. Syst. (IJEECS) 4(4), 36–41 (2016) ISSN (Online): 2347-2820
V. Raghavan, H. Shahnasser, Embedded wireless sensor network for environment monitoring. J. Adv. Comput. Netw. 3(1), 13–17 (2015)
A. Gaddam, M. Al-Hrooby, W.F. Esmael, Designing a wireless sensors network for monitoring and predicting droughts, in Proceedings of the 8th International Conference on Sensing Technology, September 2–4, Liverpool, UK, pp. 210–215 (2014)
T. Nouidui, M. Wetter, Tool coupling for the design and operation of building energy and control systems based on the functional mock-up interface standard, in Proceedings of the 10th International Modelica Conference, March 10–12, Lund, Sweden, pp. 311–320 (2014)
S. Ferdoush, X. Li, Wireless sensor network system design using Raspberry Pi and Arduino for environmental monitoring applications. Procedia Comput. Sci. 34, 103–110 (2014) (The 9th International Conference on Future Networks and Communications (FNC-2014))
H. Hadwan, P. Reddy, Smart home control by using Raspberry Pi & Arduino UNO. Int. J. Adv. Res. Comput. Commun. Eng. 5(4), 283–288 ISSN (Online) 2278-1021 (2016)
J.L.G. Ortega, L. Han, N. Whittacker, N. Bowring, A machine-learning based approach to model user occupancy and activity patterns for energy saving in buildings, in Science and Information Conference 2015, July 28–30, London, UK, pp. 474–482 (2015)
A. Capozzoli, F. Lauro, I. Khan, Fault detection analysis using data mining techniques for a cluster of smart office buildings. Expert Syst. Appl. 42, 4324–4338 (2015)
C. Fan, F. Xiao, C. Yan, A framework for knowledge discovery in massive building automation data and its application in building diagnostics. Autom. Constr. 50, 81–90 (2015)
A. Zanella, N. Bui, A. Castellani, L. Vangelista, M. Zorzi, Internet of things for smart cities. Internet Things J IEEE 1, 22–32 (2014)
S. Treado, Y. Chen, Saving building energy through advanced control strategies. Energies 6, 4769–4785 (2013)
Acknowledgements
The research received funding from the ERAF Post-doctoral Research Support Program project Nr. 1.1.1.2/16/I/001 Research application “Wireless sensor networks for a building’s energy efficiency evaluation and data exchange with the building’s management systems” Nr. 1.1.1.2./VIAA/1/16/228.
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Krūmiņš, A., Bogdanovs, N., Beļinskis, R., Mežale, K., Garjāns, M. (2019). Increasing Buildings Automation Systems Efficiency with Real-Time Simulation Trough Improved Machine Self-learning Algorithms. In: Johansson, D., Bagge, H., Wahlström, Å. (eds) Cold Climate HVAC 2018. CCC 2018. Springer Proceedings in Energy. Springer, Cham. https://doi.org/10.1007/978-3-030-00662-4_4
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