Optimal control strategies of eight parallel heat pumps using Gaussian process emulator

References

• Ahmad, M. W., M. Mourshed, B. Yuce, and Y. Rezgui. 2016. “Computational Intelligence Techniques for HVAC Systems: A Review.” Building Simulation 9 (4): 359–398. doi: 10.1007/s12273-016-0285-4
   Web of Science ®Google Scholar
• An, D., N. H. Kim, and J. H. Choi. 2015. “Practical Options for Selecting Data-Driven or Physics-Based Prognostics Algorithms with Reviews.” Reliability Engineering & System Safety 133: 223–236. doi: 10.1016/j.ress.2014.09.014
   Web of Science ®Google Scholar
• Azman, K., and J. Kocijan. 2007. “Application of Gaussian Processes for Black-Box Modelling of Biosystems.” Isa Transactions 46 (4): 443–457. doi: 10.1016/j.isatra.2007.04.001
   PubMed Web of Science ®Google Scholar
• Beghi, A., L. Cecchinato, G. Cosi, and M. Rampazzo. 2012. “A PSO-Based Algorithm for Optimal Multiple Chiller Systems Operation.” Applied Thermal Engineering 32: 31–40. doi: 10.1016/j.applthermaleng.2011.08.008
   Web of Science ®Google Scholar
• Beghi, A., L. Cecchinato, and M. Rampazzo. 2011. “A Multi-Phase Genetic Algorithm for the Efficient Management of Multi-Chiller Systems.” Energy Conversion and Management 52 (3): 1650–1661. doi: 10.1016/j.enconman.2010.10.028
   Web of Science ®Google Scholar
• Bengio, Y., and Y. Grandvalet. 2004. “No Unbiased Estimator of the Variance of K-Fold Cross-Validation.” Journal of Machine Learning Research 5: 1089–1105.
   Web of Science ®Google Scholar
• Boskoski, P., M. Gasperin, D. Petelin, and D. Juricic. 2015. “Bearing Fault Prognostics Using Rényi Entropy Based Features and Gaussian Process Models.” Mechanical Systems and Signal Processing 52-53: 327–337. doi: 10.1016/j.ymssp.2014.07.011
   Web of Science ®Google Scholar
• Cawley, G. C., and N. L. C. Talbot. 2010. “On Over-Fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation.” Journal of Machine Learning Research 11: 2079–2107.
   Web of Science ®Google Scholar
• Chan, T. S., Y. C. Chang, and J. H. Huang. 2017. “Application of Artificial Neural Network and Genetic Algorithm to the Optimization of Load Distribution for a Multiple-Type-Chiller Plant.” Building Simulation 10 (5): 711–722. doi: 10.1007/s12273-017-0363-2
   Web of Science ®Google Scholar
• Chang, Y. C. 2007. “Sequencing of Chillers by Estimating Chiller Power Consumption Using Artificial Neural Networks.” Building and Environment 42 (1): 180–188. doi: 10.1016/j.buildenv.2005.08.033
   Web of Science ®Google Scholar
• Daesung heat pump. 2019. “Heat Pump Performance Chart Book (Model #: DHAW 20-C3-01).” Accessed February 21, 2019. http://www.dsheatpump.co.kr/main/ko/index.html.
   Google Scholar
• Fan, B., X. Q. Jin, and Z. M. Du. 2011. “Optimal Control Strategies for Multi-Chiller System Based on Probability Density Distribution of Cooling Load Ratio.” Energy and Buildings 43 (10): 2813–2821. doi: 10.1016/j.enbuild.2011.06.043
   Web of Science ®Google Scholar
• Federspiel, C. C. 1997. “Estimating the Inputs of Gas Transport Processes in Buildings.” IEEE Transactions on Control Systems Technology 5 (5): 480–489. doi: 10.1109/87.623034
   Web of Science ®Google Scholar
• Fux, S. F., A. Ashouri, M. J. Benz, and L. Guzzella. 2014. “EKF Based Self-Adaptive Thermal Model for a Passive House.” Energy and Buildings 68: 811–817. doi: 10.1016/j.enbuild.2012.06.016
   Web of Science ®Google Scholar
• Heo, Y., G. Augenbroe, D. Graziano, R. T. Muehleisen, and L. Guzowski. 2015. “Scalable Methodology for Large Scale Building Energy Improvement: Relevance of Calibration in Model-Based Retrofit Analysis.” Building and Environment 87: 342–350. doi: 10.1016/j.buildenv.2014.12.016
   Web of Science ®Google Scholar
• Heo, Y., R. Choudhary, and G. A. Augenbroe. 2012. “Calibration of Building Energy Models for Retrofit Analysis under Uncertainty.” Energy and Buildings 47: 550–560. doi: 10.1016/j.enbuild.2011.12.029
   Web of Science ®Google Scholar
• Heo, Y., and V. M. Zavala. 2012. “Gaussian Process Modeling for Measurement and Verification of Building Energy Savings.” Energy and Buildings 53: 7–18. doi: 10.1016/j.enbuild.2012.06.024
   Web of Science ®Google Scholar
• Kim, D. W., and C. S. Park. 2017. “Application of Kalman Filter for Estimating a Process Disturbance in a Building Space.” Sustainability 9 (10): 1868. doi: 10.3390/su9101868
   Web of Science ®Google Scholar
• Kohavi, R. 1995. “A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection.” Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI).
   Google Scholar
• Kumar, R., R. K. Aggarwal, and J. D. Sharma. 2013. “Energy Analysis of a Building Using Artificial Neural Network: A Review.” Energy and Buildings 65: 352–358. doi: 10.1016/j.enbuild.2013.06.007
   Web of Science ®Google Scholar
• Li, Z. W., G. S. Huang, and Y. J. Sun. 2014. “Stochastic Chiller Sequencing Control.” Energy and Buildings 84: 203–213. doi: 10.1016/j.enbuild.2014.07.072
   Web of Science ®Google Scholar
• Li, Z. P., and T. Z. Liu. 2017. “Improved Particle Filter Based Soft Sensing of Room Cooling Load.” Energy and Buildings 142: 56–61. doi: 10.1016/j.enbuild.2017.03.010
   Web of Science ®Google Scholar
• Liao, Y. D., Y. J. Sun, and G. S. Huang. 2015. “Robustness Analysis of Chiller Sequencing Control.” Energy Conversion and Management 103: 180–190. doi: 10.1016/j.enconman.2015.06.060
   Web of Science ®Google Scholar
• Lipu, M. S. H., M. A. Hannan, A. Hussain, M. M. Hoque, P. J. Ker, M. H. M. Saad, and A. Ayob. 2018. “A Review of State of Health and Remaining Useful Life Estimation Methods for Lithium-Ion Battery in Electric Vehicles: Challenges and Recommendations.” Journal of Cleaner Production 205: 115–133. doi: 10.1016/j.jclepro.2018.09.065
   Web of Science ®Google Scholar
• Pedregosa, F., G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, et al. 2011. “Scikit-learn: Machine Learning in Python.” Journal of Machine Learning Research 12: 2825–2830.
   Web of Science ®Google Scholar
• Rasmussen, Carl Edward, and Christopher K. I. Williams. 2006. Gaussian Processes for Machine Learning, Adaptive Computation and Machine Learning. Cambridge, MA: MIT Press.
   Google Scholar
• Raza, M. Q., and A. Khosravi. 2015. “A Review on Artificial Intelligence Based Load Demand Forecasting Techniques for Smart Grid and Buildings.” Renewable and Sustainable Energy Reviews 50: 1352–1372. doi: 10.1016/j.rser.2015.04.065
   Web of Science ®Google Scholar
• Reddy, T. A., and K. K. Andersen. 2002. “An Evaluation of Classical Steady-State Off-Line Linear Parameter Estimation Methods Applied to Chiller Performance Data.” Hvac&R Research 8 (1): 101–124. doi: 10.1080/10789669.2002.10391291
   Web of Science ®Google Scholar
• Roberts, S., M. Osborne, M. Ebden, S. Reece, N. Gibson, and S. Aigrain. 2013. “Gaussian Processes for Time-Series Modelling.” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371 (1984): 20110550. doi: 10.1098/rsta.2011.0550
   PubMed Web of Science ®Google Scholar
• Sun, A. Y., D. B. Wang, and X. L. Xu. 2014. “Monthly Streamflow Forecasting Using Gaussian Process Regression.” Journal of Hydrology 511: 72–81. doi: 10.1016/j.jhydrol.2014.01.023
   Web of Science ®Google Scholar
• Yoon, S. H., C. S. Park, and G. Augenbroe. 2011. “On-line Parameter Estimation and Optimal Control Strategy of a Double-Skin System.” Building and Environment 46 (5): 1141–1150. doi: 10.1016/j.buildenv.2010.12.001
   Web of Science ®Google Scholar