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Supervisory optimal control using machine learning for building thermal comfort

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Języki publikacji
EN
Abstrakty
EN
For the past few decades, control and building engineering communities have been focusing on thermal comfort as a key factor in designing sustainable building evaluation methods and tools. However, estimating the indoor air temperature of buildings is a complicated task due to the nonlinear and complex building dynamics characterised by the time-varying environment with disturbances. The primary focus of this paper is designing a predictive and probabilistic room temperature model of buildings using Gaussian processes (GPs) and incorporating it into model predictive control (MPC) to minimise energy consumption and provide thermal comfort satisfaction. The full probabilistic capabilities of GPs are exploited from two perspectives: the mean prediction is used for the room temperature model, while the uncertainty is involved in the MPC objective not to lose the desired performance and design a robust controller. We illustrated the potential of the proposed method in a numerical example with simulation results.
Rocznik
Strony
1--15
Opis fizyczny
Bibliogr. 40 poz., rys.
Twórcy
  • Department of Automatic Control and Computer Engineering, Turin Polytechnic University in Tashkent, Tashkent, Uzbekistan
  • Department of Automatic Control and Computer Engineering, Turin Polytechnic University in Tashkent, Tashkent, Uzbekistan
  • Department of Computer Engineering, Tashkent University of Information Technologies, Tashkent, Uzbekistan
  • Department of Computer Engineering, Tashkent University of Information Technologies, Tashkent, Uzbekistan
  • Department of Automatic Control and Computer Engineering, Turin Polytechnic University in Tashkent, Tashkent, Uzbekistan
Bibliografia
  • [1] Abdufattokhov, S., Ibragimova, K., and Gulyamova, D. The applicability of machine learning algorithms in predictive modeling for sustainable energy management. In Forthcoming Networks and Sustainability in the IoT Era, F. Al-Turjman and J. Rasheed, Eds., Springer International Publishing, Cham, 2022, pp. 379–391.
  • [2] Abdufattokhov, S., Ibragimova, K., Gulyamova, D., and Tulaganov, K. Gaussian processes regression based Energy system identification of manufacturing process for model predictive control. International Journal of Emerging Trends in Engineering Research 8, 9 (2020), 4927–4932.
  • [3] Abdufattokhov, S., and Muhiddinov, B. Probabilistic approach for system identification using machine learning. In 2019 International Conference on Information Science and Communications Technologies (ICISCT), IEEE, Tashkent, 2019, pp. 1–4.
  • [4] Afram, A., and Janabi-Sharifi, F. Theory and applications of HVAC control systems - a review of model predictive control (MPC). Building and Environment 72 (2014), 343–355.
  • [5] Andersson, J. A. E., Gillis, J., Horn, G., Rawlings, J. B., and Diehl, M. CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11 (2019), 1–36.
  • [6] A.Wachter, and Biegler, L. T. ¨ On the implementation of a primal-dual interior point filter line search algorithm for large-scale nonlinear programming. Mathematical Programming 106, 1 (2006), 25–57.
  • [7] Azman, K., and Kocijan, J. Application of Gaussian processes for black-box modelling of biosystems. ISA Transactions 46, 4 (2007), 443–457.
  • [8] Borrelli, F., Bemporad, A., and Morari M. Predictive control for linear and hybrid systems. Cambridge University Press, 2017.
  • [9] Brik, B., Esseghir, M., Merghem-Boulahia, L., and Snoussi, H. ThermCont: A machine Learning enabled Thermal Comfort Control Tool in a real time. In 2019 15th International Wireless Communications and Mobile Computing Conference (IWCMC), Tangier, Morocco, 2019, IEEE, pp. 294–300.
  • [10] Brik, B., Esseghir, M., Merghem-Boulahia, L., and Snoussi, H. An IoT-based deep learning approach to analyse indor thermal comfort of disabled people. Building and Environment 203, (2021), 108056.
  • [11] Chen, X., Wang, Q., and Srebric, J. A data-driven state-space model of indoor thermal sensation using occupant feedback for low-energy buildings. Energy and Buildings 91, (2015), 187–198.
  • [12] Dounis, A. I, and Caraiscos, C. Advanced control systems engineering for energy and comfort management in a building environment—a review. Renewable and Sustainable Energy Reviews 13, 6-7 (2009), 1246–1261.
  • [13] Fang, X., Gong, G., Li, G., Chun, L., Peng, P., Li, W., Shi, X., and Chen, X. Deep reinforcement learning optimal control strategy for temperature setpoint real-time reset in multi-zone building HVAC system. Applied Thermal Engineering 212, (2022), 118552.
  • [14] Fanger, P. O. Thermal comfort. analysis and applications in environmental engineering. Thermal comfort. Analysis and applications in environmental engineering. Danish Technical Press, Copenhagen, Denmark, 1970.
  • [15] Fayyaz, M., Farhan, A. A., and Javed, A. R. Thermal comfort model for HVAC buildings using machine learning. Arabian Journal for Science and Engineering 47 (2022), 2045–2060.
  • [16] Gauthier, S. The role of environmental and personal variables in influencing thermal comfort indices used in building simulation. In Proceedings of BS2013: 13th Conference of International Building Performance Simulation Association, Chambery, France, August 26-28 (2013), IBPSA, pp. 2320–2325.
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  • [18] Halawa, E., and Hoof, J. V. The adaptive approach to thermal comfort: A critical overview. Energy and Buildings 51 (2012), 101–110.
  • [19] Henze, G. P., Kalz, D. E., Felsmann, C., and Knabe, G. Impact of forecasting accuracy on predictive optimal control of active and passive building thermal storage inventory. HVAC&R Research 10, 2 (2004), 153–178.
  • [20] Hsiao, Y. H. Household electricity demand forecast based on context information and user daily schedule analysis from meter data. IEEE Transactions on Industrial Informatics 11, 1 (2015), 33–43.
  • [21] Jovanovic, R. Z., Sretenovic, A. A., and Zivkovic, B. D. Ensemble of various neural networks for prediction of heating energy consumption. Energy and Buildings 94 (2015), 189–199.
  • [22] Kocijan, J. Modelling and control of dynamic systems using Gaussian process models, Springer International Publishing, Cham, 2016.
  • [23] Kumar, T. M. S., and Kurian, C. P. Real-time data based thermal comfort prediction leading to temperature setpoint control. Journal of Ambient Intelligence and Humanized Computing, (2022).
  • [24] Kusiak, A., Li, M., and Zhang, Z. A data-driven approach for steam load prediction in buildings. Applied Energy 87, 3 (2010), 925–933.
  • [25] Li, Y., O’Neill, Z., Zhang, L., Chen, J., Im, P., and DeGraw, J. Grey-box modeling and application for building Energy simulations - A critical review. Renewable and Sustainable Energy Reviews 146 (2021), 111174.
  • [26] Liang, J., and Du, R. Thermal comfort control based on neural network for HVAC application. In Proceedings of 2005 IEEE Conference on Control Applications, 2005. CCA 2005., Toronto, 2005, IEEE, pp. 819–824.
  • [27] Liu, Y., Wang, W., and Ghadimi, N. Electricity load forecasting by an improved forecast engine for building level consumers. Energy 139 (2017), 18–30.
  • [28] Lute, P. J. The use of predictions in temperature control in buildings. A passive climate system application. PhD thesis, Delft University of Technology, 1992.
  • [29] Oldewurtel, F., Parisio, A., Jones, C. N., Gyalistras, D., Gwerder, M., Stauch, V., Lehmann, B., and Morari, M. Use of model predictive control and weather forecasts for energy efficient building climate control. Energy and Buildings 45 (2012), 15–27.
  • [30] Rasmussen, C. E., and Williams, C. K. I. Gaussian processes for machine learning. MIT Press, Cambridge, MA, 2006.
  • [31] Schirrer, A., Brandstetter, M., Leobner, I., Hauer, S., and Kozek, M. Nonlinear model predictive control for a heating and cooling system of a low-energy office building. Energy and Buildings 125 (2016), 86–98.
  • [32] Serale, G., Fiorentini, M., Capozzoli, A., Bernardini, D., and Bemporad, A. Model Predictive Control (MPC) for enhancing building and HVAC system energy efficiency: Problem formulation, applications and opportunities. Energies 11, 3 (2018), 631.
  • [33] Smarra, F., Jain, A., de Rubeis, T., Ambrosini, D., D’Innocenzo, A., and Mangharam, R. Data-driven model predictive control using random forests for building energy optimization and climate control. Applied Energy 226 (2018), 1252–1272.
  • [34] Solak, E., Murray-Smith, R., Leithead, W. E., Leith, D. J., and Rasmussen, C. E. Derivative observations in Gaussian process models of dynamic systems. In Advances in Neural Information Processing Systems 15. Proceedings of the 2002 Conference, S. Becker, Thrun S. and Obermayer K., Eds., MIT Press, Cambridge, MA, 2003, pp. 1057–1064.
  • [35] Sun, G., Jiang, C., Wang, X., and Yang, X. Short-term building load forecast based on a data mining feature selection and LSTM-RNN method. IEEJ Transactions on Electrical and Electronic Engineering 15, 7 (2020), 1002–1010.
  • [36] Thompson, K. R. Implementation of Gaussian process models for non-linear system identification. PhD thesis, University of Glasgow, 2009.
  • [37] Wang, J., and Zhao, T. Event-driven online decoupling control mechanism for the variable flow rate HVAC system based on the medium response properties. Building and Environment 218 (2022), 109104.
  • [38] Wang, Y., Ocampo-Martınez, C., Puig, V., and Quevedo, J. Gaussian-process-based demand forecasting for predictive control of drinking water networks. In Critical information infrastructures security. CRITIS 2014, C. G. Panayiotou, G. Ellinas, E.Kyriakides and M. M. Polycarpou, Eds., vol. 8985 of Lecture Notes in Computer Science, 2016, Springer, Cham, pp. 69–80.
  • [39] Xu, H., He, Y., Sun, X., He, J., and Xu, Q. Prediction of thermal energy inside smart homes using IoT and classifier ensemble techniques. Computer Communications 151 (2020), 581–589.
  • [40] Zhang, L., Wen, J., Li, Y., Chen, J., Ye, Y., Fu, Y., and Livingood, W. C. A review of machine learning in building load prediction. Applied Energy 285 (2021), 116452.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-f11b4941-5274-4bdf-98df-091534f9d3c5
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