Stochastic learning and control of building dynamics for thermal comfort

[1] S. Abdufattokhov, K. Ibragimova, D. Gulyamova, and K. Tulaganov, Gaussian processes regression based energy system identification of manufacturing process for model predictive control, Int. J. Emerg. Trends Engin. Res. 8 (2020), no. 9, 4927–4932.

[2] S. Abdufattokhov and B. Muhiddinov, Probabilistic approach for system identification using machine learning, Int. Conf. Inf. Sci. Commun. Technol. (ICISCT), 2019, pp. 1–4.

[3] A. Afram and F. Janabi-Sharifi, Theory and applications of HVAC control systems: A review of model predictive control (MPC), Build. Envir. 72 (2014), 343–355.

[4] J.A.E. Andersson, J. Gillis, G. Horn, J.B. Rawlings, and M. Diehl, CasADi: a software framework for nonlinear optimization and optimal control, Math. Prog. Comput. 11 (2019), no. 1.

[5] K. Aˇzman and J. Kocijan, Application of Gaussian processes for black-box modelling of biosystems, ISA Transactions 46 (2007), no. 4, 443–457.

[6] F. Borrelli, A. Bemporad, and M. Morari, Predictive Control for Linear and Hybrid Systems, Cambridge University Press, 2017.

[7] X. Chen, Q. Wang, and J. Srebric, A data-driven state-space model of indoor thermal sensation using occupant feedback for low-energy buildings, Energy Build. 91 (2015), 187–198.

[8] A.I. Dounis and C. Caraiscos, Advanced control systems engineering for energy and comfort management in a building environment—A review, Renew. Sustain. Energy Rev. 13 (2009), no. 6, 1246–1261.

[9] C.N. Jones D. Gyalistras M. Gwerder V. Stauch B. Lehmann F. Oldewurtel, A. Parisio and M. Morari, Use of model predictive control and weather forecasts for energy efficient building climate control, Energy Build. 45 (2012), 15–27.

[10] T. de Rubeis D. Ambrosini A. D’Innocenzo F. Smarra, A. Jain and R. Mangharam, Data-driven model predictive control using random forests for building energy optimization and climate control, Appl. Energy 226 (2018), 1252–1272.

[11] P.O. Fanger, Thermal comfort. analysis and applications in environmental engineering., McGraw-Hill, New York, 1970.

[12] S. Gauthier, The role of environmental and personal variables in influencing thermal comfort indices used in building simulation, Proc. BS2013: 13th Conf. Int. Build. Perform. Simul. Assoc. Chambery, France, August 26-28, IBPSA, 2013, pp. 2320–2325.

[13] M. Gwerder and J. T¨odtli, Predictive control for thermal storage management in buildings, 8th REHVA World Cong. Build. Technol. CLIMA, no. October, 2005, pp. 1–6.

[14] E. Halawa and J. Van Hoof, The adaptive approach to thermal comfort: A critical overview, Energy Build. 51 (2012), 101–110.

[15] Y.H. Hsiao, Household electricity demand forecast based on context information and user daily schedule analysis from meter data, IEEE Trans. Ind. Inf. 11 (2015), no. 1, 33–43.

[16] R.Z. Jovanovic, A.A. Sretenovic, and B.D. Zivkovic, Ensemble of various neural networks for prediction of heating energy consumption, Energy Build. 94 (2015), no. Complete, 189–199.

[17] Jus Kocijan, Modelling and control of dynamic systems using Gaussian process models, 2016.

[18] A. Kusiak, M. Li, and Z. Zhang, A data-driven approach for steam load prediction in buildings, Appl. Energy 87 (2010), 925–933.

[19] Y. Li, Z. O’Neill, L. Zhang, J. Chen, P. Im, and J. DeGraw, Grey-box modeling and application for building energy simulations: A critical review, Renew. Sustain. Energy Rev. 146 (2021), no. C.

[20] J. Liang and R. Du, Thermal comfort control based on neural network for HVAC application, Proc. IEEE Conf. Control Appl. CCA (2005), 819–824.

[21] G. Serale, M. Fiorentini, A. Capozzoli, D. Bernardini, and A. Bemporad, Model predictive control (MPC) for enhancing building and HVAC system energy efficiency: Problem formulation, applications and opportunities, Energies 11 (2018), no. 3.

[22] E. Solak, R. Murray-Smith, W. E. Leithead, D. J. Leith, and C. E. Rasmussen, Derivative observations in Gaussian Process Models of Dynamic Systems, Advances in Neural Information Processing Systems, 2003.

[23] G. Sun, C. Jiang, X.Wang, and X. Yang, Short-term building load forecast based on a data mining feature selection and lstm-rnn method, IEEJ Trans. Electr. Electronic Eng. 15 (2020), 1002–1010.

[24] K.R. Thompson, Implementation of Gaussian process models for nonlinear system identification, Thesis, University of Glasgow, 2009.

[25] Y. Wang, C. Ocampo-Martınez, V. Puig, and J. Quevedo, Gaussian-Process-Based Demand Forecasting for Predictive Control of Drinking Water Networks, vol. 8985, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer, 2016.

[26] C.K.I. Williams and C.E. Rasmussen, Gaussian Processes for Machine Learning, The MIT Press, Cambridge, MA, USA, 2006.

[27] A. Wachter and L.T. Biegler, On the implementation of a primal-dual interior point filter line search algorithm for large-scale nonlinear programming, Math. Programm. 106 (2006), no. 1, 25–57.

[28] H. Xu, Y. He, X. Sun, J. He, and Q. Xu, Prediction of thermal energy inside smart homes using IoT and classifier ensemble techniques, Comput. Commun. 151 (2020), 581–589.

[29] W. Wang Y. Liu and N. Ghadimi, Electricity load forecasting by an improved forecast engine for building level consumers, Energy 139 (2017), 18–30.

[30] L. Zhang, J. Wen, Y. Li, J. Chen, Y. Ye, Y. Fu, and W.C. Livingood, A review of machine learning in building load prediction, Appl. Energy 285 (2021), 116–452.