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Articles

State-space models for building control: how deep should you go?

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Pages 707-719 | Received 12 Jun 2020, Accepted 22 Aug 2020, Published online: 14 Sep 2020

References

  • Abadi, M., A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, et al. 2015. “TensorFlow: Large-scale Machine Learning on Heterogeneous Systems.” Software available from tensorflow.org.
  • Amos, B., L. Xu, and J. Z. Kolter. 2017. “Input Convex Neural Networks”. Proceedings of the International Conference on Machine Learning (ICML), Sydney, Australia, 146–155.
  • Bieker, K., S. Peitz, S. L. Brunton, J. N. Kutz, and M. Dellnitz. 2020. “Deep Model Predictive Flow Control with Limited Sensor Data and Online Learning.” Theoretical and Computational Fluid Dynamics 34: 577–591. doi: 10.1007/s00162-020-00520-4
  • Camacho, E. F., and C. Bordons. 2007. Model Predictive Control. London: Springer-Verlag.
  • Chakraborty, D., and H. Elzarka. 2019. “Advanced Machine Learning Techniques for Building Performance Simulation: A Comparative Analysis.” Journal of Building Performance Simulation 12 (2): 193–207. doi: 10.1080/19401493.2018.1498538
  • Chen, Y., Y. Shi, and B. Zhang. 2017. “Modeling and Optimization of Complex Building Energy Systems with Deep neural Networks.” In 51st Asilomar Conference on Signals, Systems, and Computers, ACSSC 2017, Pacific Grove, CA, USA, October 29–November 1, 2017, 1368–1373.
  • Chen, Y., Y. Shi, and B. Zhang. 2019. “Optimal Control via Neural Networks: A Convex Approach.” Proceedings of the International Conference on Learning Representations (ICLR), New Orleans, USA.
  • Chen, Y., Y. Shi, and B. Zhang. 2020. “Input Convex Neural Networks for Optimal Voltage Regulation.” Preprint, arXiv:2002.08684.
  • Chung, J., C. Gulcehre, K. Cho, and Y. Bengio. 2014, December. “Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling.” Proceedings of the NIPS 2014 Workshop on Deep Learning and Representation Learning, Montreal, Canada.
  • Crawley, D. B., L. K. Lawrie, C. O. Pedersen, and F. C. Winkelmann. 2000. “Energy Plus: Energy Simulation Program.” ASHRAE Journal 42 (4): 49–56.
  • Drgoňa, J., D. Picard, M. Kvasnica, and L. Helsen. 2018. “Approximate Model Predictive Building Control Via Machine Learning.” Applied Energy 218 (23): 199–216. doi: 10.1016/j.apenergy.2018.02.156
  • European Standard. 2011. “Heat Pumps with Electrically Driven Compressors-Testing and Requirement for Marking of Domestic Hot Water Units.” EN 16147, January 2011.
  • Fletcher, R. 1987. Practical Methods of Optimization. 2nd ed. New York: John Wiley & Sons.
  • Funahashi, K., and Y. Nakamura. 1993. “Approximation of Dynamical Systems by Continuous Time Recurrent Neural Networks.” Neural Networks 6 (6): 801–806. doi: 10.1016/S0893-6080(05)80125-X
  • Gill, P. E., and E. Wong. 2012. “Sequential Quadratic Programming Methods.” In Mixed Integer Nonlinear Programming, 147–224. New York, NY: Springer
  • Gonzalez, J., and W. Yu. 2018. “Non-Linear System Modeling Using Lstm Neural Networks.” IFAC-PapersOnLine 51 (13): 485–489. 2nd IFAC Conference on Modelling, Identification and Control of Nonlinear Systems MICNON 2018. doi: 10.1016/j.ifacol.2018.07.326
  • Gorecki, T. 2017. “Predictive Control methods for Building Control and Demand Response.” PhD thesis, Ecole Polytechnique Fédérale de Lausanne.
  • Hochreiter, S., and J. Schmidhuber. 1997. “Long Short-Term Memory.” Neural Computation 9 (8): 1735–1780. doi: 10.1162/neco.1997.9.8.1735
  • Lam, S. K., A. Pitrou, and S. Seibert. 2015. “Numba: A Llvm-Based Python Jit Compiler.” Proceedings of the Second Workshop on the LLVM Compiler Infrastructure in HPC, San Jose, CA, USA, 1–6.
  • Lanzetti, N., Y. Z. Lian, A. Cortinovis, L. Dominguez, M. Mercangöz, and C. Jones. 2019, June. “Recurrent Neural Network Based MPC for Process Industries.” 18th European Control Conference (ECC), Naples, Italy, 1005–1010.
  • Ljung, L. 1999. “System Identification.” Wiley Encyclopedia of Electrical and Electronics Engineering. Hoboken, NJ: Wiley
  • Maddalena, E. T., C. G. da S. Moraes, G. Waltrich, and C. N. Jones. 2019. “A Neural Network Architecture to Learn Explicit MPC Controllers from Data.” Preprint, arXiv:1911.10789.
  • Mayne, D. Q. 2014. “Model Predictive Control: Recent Developments and Future Promise.” Automatica50 (12): 2967–2986. doi: 10.1016/j.automatica.2014.10.128
  • Nakkiran, P., G. Kaplun, Y. Bansal, T. Yang, B. Barak, and I. Sutskever. 2020. “Deep Double Descent: Where Bigger Models and More Data Hurt.” Proceedings of the International Conference on Learning Representations (ICLR), Addis Ababa, Ethiopia.
  • Ogunmolu, O., X. Gu, S. Jiang, and N. Gans. 2016. “Nonlinear Systems Identification Using Deep Dynamic Neural Networks.” In Proceedings of the of the IEEE International Conference on Information and Automation (ICIA), August 2016, Yunnan, China, 8–10.
  • Papadopoulos, S., E. Azar, Wei-Lee Woon, and C. E. Kontokosta. 2018. “Evaluation of Tree-Based Ensemble Learning Algorithms for Building Energy Performance Estimation.” Journal of Building Performance Simulation 11 (3): 322–332. doi: 10.1080/19401493.2017.1354919
  • Péan, T. Q., J. Salom, and R. Costa-Castelló. 2019. “Review of Control Strategies for Improving the Energy Flexibility Provided by Heat Pump Systems in Buildings.” Journal of Process Control 74: 35–49. doi: 10.1016/j.jprocont.2018.03.006
  • Pedregosa, F., G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, and J. Vanderplas. (Oct) 2011. “Scikit-Learn: Machine Learning in Python.” Journal of Machine Learning Research 12: 2825–2830.
  • Peng, H., K. Nakano, and H. Shioya. 2007. “Nonlinear Predictive Control Using Neural Nets-based Local Linearization Arx Model–Stability and Industrial Application.” IEEE Transactions on Control Systems Technology 15 (1): 130–143. doi: 10.1109/TCST.2006.883339
  • Schoukens, J., and L. Ljung. 2019. “Nonlinear System Identification: A User-Oriented Roadmap.” IEEE Control Systems Magazine 39 (6): 28–99.
  • Sourbron, M., C. Verhelst, and L. Helsen. 2013. “Building Models for Model Predictive Control of Office Buildings with Concrete Core Activation.” Journal of Building Performance Simulation 6 (3): 175–198. doi: 10.1080/19401493.2012.680497
  • Stein, J., W. Holmgren, J. Forbess, and C. W. Hansen. 2016. “Pvlib: Open Source Photovoltaic Performance Modeling Functions for Matlab and Python.” Proceedings of the 2016 IEEE 43rd Photovoltaic Specialists Conference (PVSC), Portland, USA, 3425–3430.
  • Sturzenegger, D., D. Gyalistras, M. Morari, and R. S. Smith. 2016. “Model Predictive Climate Control of A Swiss Office Building: Implementation, Results, and Cost–Benefit Analysis.” IEEE Transactions on Control Systems Technology 24 (1): 1–12. doi: 10.1109/TCST.2015.2415411
  • Taddeo, P., A. Colet, R. E. Carrillo, L. C. Canals, B. Schubnel, Y. Stauffer, I. Bellanco, C. C. Garcia, and J. Salom. January 2020. “Management and Activation of Energy Flexibility At Building and Market Level: A Residential Case Study.” Energies 13 (5): 1188. doi: 10.3390/en13051188
  • Valenzuela, P. E., A. Ebadat, N. Everitt, and A. Parisio. 2020. “Closed-Loop Identification for Model Predictive Control of HVAC Systems: From Input Design to Controller Synthesis.” IEEE Transactions on Control Systems Technology 28 (5): 1681–1695. doi: 10.1109/TCST.2019.2917675
  • Van Overschee, P., and B. L. De Moor. 2012. Subspace Identification For Linear Systems: Theory–Implementation–Applications. New York, NY: Springer Science & Business Media.
  • Zhang, J., T. He, S. Sra, and A. Jadbabaie. 2019. “Analysis of Gradient Clipping and Adaptive Scaling with A Relaxed Smoothness Condition.” Preprint, arXiv:1905.11881.

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