Automatically generated by Mendeley Desktop 1.19.8 Any changes to this file will be lost if it is regenerated by Mendeley. @inproceedings{soton, booktitle = {Proceedings of BS2013: 13th Conference of International Building Performance Simulation Association, Chamb{\'e}ry, France, August 26-28}, title = {The role of environmental and personal variables in influencing thermal comfort indices used in building simulation}, author = {S. Gauthier}, publisher = {IBPSA}, year = {2013}, pages = {2320--2325}, } @article{HalHof, title = {The adaptive approach to thermal comfort: A critical overview}, author = {E. Halawa and J. Van Hoof}, journal = {Energy and Buildings}, volume = {51}, pages = {101--110}, year = {2012}, doi = {http://dx.doi.org/10.1016/j.enbuild.2012.04.011}, } @article{Liang2005, title={Thermal comfort control based on neural network for HVAC application}, author={Jian Liang and Ruxu Du}, journal={Proceedings of 2005 IEEE Conference on Control Applications, 2005. CCA 2005.}, year={2005}, pages={819--824}, doi={https://doi.org/10.1109/CCA.2005.1507230} } @Article{LiZhLi, author={Li, Yanfei and O'Neill, Zheng and Zhang, Liang and Chen, Jianli and Im, Piljae and DeGraw, Jason}, title={{Grey-box modeling and application for building energy simulations - A critical review}}, journal={Renewable and Sustainable Energy Reviews}, year=2021, volume={146}, number={C}, pages={}, month={}, doi={10.1016/j.rser.2021.11117}, } @Article{Hsiao, author={Y.H. Hsiao}, title={{Household electricity demand forecast based on context information and user daily schedule analysis from meter data}}, journal={IEEE Trans. Ind. Inf.}, year=2015, volume={11}, number={1}, pages={33--43} } @article{Afram2014, title = {Theory and applications of HVAC control systems - A review of model predictive control (MPC)}, journal = {Building and Environment}, volume = {72}, pages = {343--355}, year = {2014}, doi = {https://doi.org/10.1016/j.buildenv.2013.11.016}, author = {A. Afram and F. Janabi-Sharifi}, } @article{Smarra2018, title={Data-driven model predictive control using random forests for building energy optimization and climate control}, author={Francesco Smarra and Achin Jain and Tullio de Rubeis and Dario Ambrosini and Alessandro D'Innocenzo and Rahul Mangharam}, journal={Applied Energy}, year={2018}, volume={226}, pages={1252--1272} } @article{Xu2020, title={Prediction of thermal energy inside smart homes using IoT and classifier ensemble techniques}, author={Hanqi Xu and Yuan He and Xiang Sun and Jia He and Qiongmei Xu}, journal={Comput. Commun.}, year={2020}, volume={151}, pages={581-589}, doi={ttps://doi.org/10.1016/j.comcom.2019.12.020} } @article{OLDEWURTEL2012, title = {Use of model predictive control and weather forecasts for energy efficient building climate control}, journal = {Energy and Buildings}, volume = {45}, pages = {15--27}, year = {2012}, issn = {0378-7788}, doi = {https://doi.org/10.1016/j.enbuild.2011.09.022}, author = {Frauke Oldewurtel and Alessandra Parisio and Colin N. Jones and Dimitrios Gyalistras and Markus Gwerder and Vanessa Stauch and Beat Lehmann and Manfred Morari}, } @Article{SGFAB, AUTHOR = {Serale, Gianluca and Fiorentini, Massimo and Capozzoli, Alfonso and Bernardini, Daniele and Bemporad, Alberto}, TITLE = {Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities}, JOURNAL = {Energies}, VOLUME = {11}, YEAR = {2018}, NUMBER = {3}, DOI = {10.3390/en11030631} } @article{DOUNIS2009, author = {A.I. Dounis and C. Caraiscos}, title = {Advanced control systems engineering for energy and comfort management in a building environment—A review}, journal = {Renewable and Sustainable Energy Reviews}, volume = {13}, number = {6}, pages = {1246-1261}, year = {2009}, doi = {https://doi.org/10.1016/j.rser.2008.09.015}, } @article{Liu2017ELF, title={Electricity load forecasting by an improved forecast engine for building level consumers}, author={Y. Liu and Wei Wang and Noradin Ghadimi}, journal={Energy}, year={2017}, volume={139}, pages={18-30} } @article{Jovanovic2015, author = {Jovanovic, Radisa Z. and Sretenovic, Aleksandra A. and Zivkovic, Branislav D.}, doi = {10.1016/j.enbuild.2015.02.052}, journal = {Energy and Buildings}, number = {Complete}, pages = {189-199}, title = {Ensemble of various neural networks for prediction of heating energy consumption}, volume = {94}, year = {2015}, doi={10.1016/j.enbuild.2015.02.052} } @article{Sun2020, title={Short-term building load forecast based on a data mining feature selection and LSTM-RNN method}, author={G. Sun and C. Jiang and X. Wang and X. Yang}, journal={IEEJ Trans. Electr. Electronic Eng.}, year={2020}, volume={15}, pages={1002-1010} } @article{Kusiak2010ADA, title={A data-driven approach for steam load prediction in buildings}, author={Andrew Kusiak and Mingyang Li and Zijun Zhang}, journal={Applied Energy}, year={2010}, volume={87}, pages={925-933} } @article{Zhang2021ARO, title={A review of machine learning in building load prediction}, author={Liang Zhang and Jin Wen and Yanfei Li and Jianli Chen and Yunyang Ye and Yangyang Fu and William C. Livingood}, journal={Applied Energy}, year={2021}, volume={285}, pages={116452} } @book{BBM17, title = "Predictive Control for Linear and Hybrid Systems", author = {F. Borrelli and A. Bemporad and M.Morari}, year = 2017, publisher = "Cambridge University Press", } @article{CHEN2015, title = {A data-driven state-space model of indoor thermal sensation using occupant feedback for low-energy buildings}, author = {Xiao Chen and Qian Wang and Jelena Srebric}, journal = {Energy and Buildings}, volume = {91}, pages = {187-198}, year = {2015}, doi = {https://doi.org/10.1016/j.enbuild.2015.01.038}, } @article{fanger1970thermal, title={Thermal comfort. Analysis and applications in environmental engineering.}, author={Fanger, Poul O and others}, journal={Thermal comfort. Analysis and applications in environmental engineering.}, year={1970}, publisher={Copenhagen: Danish Technical Press.} } @article{Galiana1974, abstract = {The three step identification process of model development, parameter estimation, and performance analysis is illustrated through the identification of models for the prediction of electric power demand. Each step is carefully supported by numerical results based on physical data. Three types of progressively more complex but more accurate load models are identified which describe 1) time periodicity, 2) time periodicity plus load autocorrela-tion, and 3) time periodicity plus load autocorrelation plus dynamic temperature effects. Accurate predictions up to one week are demonstrated. General guidelines are extrapolated from this identification example when possible. {\textcopyright} 1974, IEEE. All rights reserved.}, author = {Galiana, Francisco D. and Handschin, Edmund and Fiechter, Albert R.}, doi = {10.1109/TAC.1974.1100724}, file = {:D$\backslash$:/my research/publication/2020/Advances in Science Technology and Engineering Systems/References/01100724.pdf:pdf}, issn = {15582523}, journal = {IEEE Transactions on Automatic Control}, number = {6}, title = {{Identification of Stochastic Electric Load Models from Physical Data}}, volume = {19}, year = {1974} } @article{Gregorcic2008, abstract = {Neural networks have been widely used to model nonlinear systems for control. The curse of dimensionality and lack of transparency of such neural network models has forced a shift towards local model networks and recently towards the nonparametric Gaussian processes approach. Assuming common validity functions, all of these models have a similar structure. This paper examines the evolution from the radial basis function network to the local model network and finally to the Gaussian process model. A simulated example is used to explain the advantages and disadvantages of each structure. {\textcopyright} 2007 Elsevier Ltd. All rights reserved.}, author = {Gregor{\v{c}}i{\v{c}}, Gregor and Lightbody, Gordon}, doi = {10.1016/j.engappai.2007.11.004}, file = {:C$\backslash$:/Users/Shoxjahon/Downloads/1-s2.0-S0952197607001467-main.pdf:pdf}, issn = {09521976}, journal = {Engineering Applications of Artificial Intelligence}, keywords = {Gaussian processes,Local model network,Network structure,Nonlinear system identification,Radial basis function network}, number = {7}, pages = {1035--1055}, title = {{Nonlinear system identification: From multiple-model networks to Gaussian processes}}, volume = {21}, year = {2008} } @inproceedings{Solak2003, author = {Solak, E. and Murray-Smith, R. and Leithead, W. E. and Leith, D. J. and Rasmussen, C. E.}, booktitle = {Advances in Neural Information Processing Systems}, title = {{Derivative observations in Gaussian Process Models of Dynamic Systems}}, year = {2003} } @book{Rasmussen2006, author = {Rasmussen, Carl Edward and Williams, C K I}, booktitle = {The MIT Press, Cambridge, MA, USA}, number = {2}, title = {{Gaussian processes for machine learning. 2006}}, volume = {38}, year = {2006} } @article{Kouvaritakis2006, abstract = {A recent stochastic and multiobjective formulation based on static open-loop optimization has laid the foundations of a quantitative approach to sustainable development policy assessment. Here, the connections between such an approach and model predictive control are explored, and a reformulation that introduces dynamics and addresses closed loop performance and stability is proposed. The approach has wide applicability but it is hoped that it will provide sustainable development practitioners in particular with new insights. {\textcopyright} 2006 IEEE.}, author = {Kouvaritakis, Basil and Cannon, Mark and Couchman, Paul}, doi = {10.1109/TAC.2005.861702}, file = {:D$\backslash$:/my research/publication/2020/Advances in Science Technology and Engineering Systems/References/01576867.pdf:pdf}, issn = {00189286}, journal = {IEEE Transactions on Automatic Control}, number = {1}, title = {{MPC as a tool for sustainable development integrated policy assessment}}, volume = {51}, year = {2006} } @inproceedings{Patrinos2011, abstract = {We formulate the problem of dynamic, real-time optimal power dispatch for electric power systems consisting of conventional power generators, intermittent generators from renewable sources, energy storage systems and price-inelastic loads. The generation company managing the power system can place bids on the real-time energy market (the so-called regulating market) in order to balance its loads and/or to make profit. Prices, demands and intermittent power injections are considered to be stochastic processes and the goal is to compute power injections for the conventional power generators, charge and discharge levels for the storage units and exchanged power with the rest of the grid that minimize operating and trading costs. We propose a scenario-based stochastic model predictive control algorithm to solve the real-time market-based optimal power dispatch problem. {\textcopyright} 2011 IEEE.}, author = {Patrinos, Panagiotis and Trimboli, Sergio and Bemporad, Alberto}, booktitle = {Proceedings of the IEEE Conference on Decision and Control}, doi = {10.1109/CDC.2011.6160798}, file = {:D$\backslash$:/my research/publication/2020/Advances in Science Technology and Engineering Systems/References/06160798.pdf:pdf}, issn = {01912216}, title = {{Stochastic MPC for real-time market-based optimal power dispatch}}, year = {2011} } @article{Wang2016, author = {Wang, Ye and Ocampo-Mart{\'{i}}nez, Carlos and Puig, Vicen{\c{c}} and Quevedo, Joseba}, doi = {10.1007/978-3-319-31664-2_8}, journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, pages = {69--80}, title = {{Gaussian-process-based demand forecasting for predictive control of drinking water networks}}, volume = {8985}, year = {2016} } @article{Amrit2011, abstract = {In the standard model predictive control implementation, first a steady-state optimization yields the equilibrium point with minimal economic cost. Then, the deviation from the computed best steady state is chosen as the stage cost for the dynamic regulation problem. The computed best equilibrium point may not be the global minimum of the economic cost, and hence, choosing the economic cost as the stage cost for the dynamic regulation problem, rather than the deviation from the best steady state, offers potential for improving the economic performance of the system. It has been previously shown that the existing framework for MPC stability analysis, which addresses to the standard class of problems with a regulation objective, does not extend to economic MPC. Previous work on economic MPC developed new tools for stability analysis and identified sufficient conditions for asymptotic stability. These tools were developed for the terminal constraint MPC formulation, in which the system is stabilized by forcing the state to the best equilibrium point at the end of the horizon. In this work, we relax this constraint by imposing a region constraint on the terminal state instead of a point constraint, and adding a penalty on the terminal state to the regulator cost. We extend the stability analysis tools, developed for terminal constraint economic MPC, to the proposed formulation and establish that strict dissipativity is sufficient for guaranteeing asymptotic stability of the closed-loop system. We also show that the average closed-loop performance outperforms the best steady-state performance. For implementing the proposed formulation, a rigorous analysis for computing the appropriate terminal penalty and the terminal region is presented. A further extension, in which the terminal constraint is completely removed by modifying the regulator cost function, is also presented along with its stability analysis. Finally, an illustrative example is presented to demonstrate the differences between the terminal constraint and the proposed terminal penalty formulation. {\textcopyright} 2011 Elsevier Ltd. All rights reserved.}, author = {Amrit, Rishi and Rawlings, James B. and Angeli, David}, doi = {10.1016/j.arcontrol.2011.10.011}, file = {:D$\backslash$:/my research/publication/2020/Advances in Science Technology and Engineering Systems/References/1-s2.0-S136757881100040X-main.pdf:pdf}, issn = {13675788}, journal = {Annual Reviews in Control}, keywords = {Closed-loop stability,Dissipative systems,Model predictive control,Process economics,Terminal penalty}, number = {2}, pages = {178--186}, publisher = {Elsevier Ltd}, title = {{Economic optimization using model predictive control with a terminal cost}}, url = {http://dx.doi.org/10.1016/j.arcontrol.2011.10.011}, volume = {35}, year = {2011} } @book{Smirnov2005, author = {Smirnov, Michael}, booktitle = {Lecture Notes in Computer Science}, file = {:D$\backslash$:/my research/publication/2020/Advances in Science Technology and Engineering Systems/References/2005{\_}Book{\_}.pdf:pdf}, isbn = {3540244573}, issn = {03029743}, title = {{Lecture Notes in Computer Science: Preface}}, volume = {3457}, year = {2005} } @misc{Kocijan2016, abstract = {This monograph opens up new horizons for engineers and researchers in academia and in industry dealing with or interested in new developments in the field of system identification and control. It emphasizes guidelines for working solutions and practical advice for their implementation rather than the theoretical background of Gaussian process (GP) models. The book demonstrates the potential of this recent development in probabilistic machine-learning methods and gives the reader an intuitive understanding of the topic. The current state of the art is treated along with possible future directions for research. Systems control design relies on mathematical models and these may be developed from measurement data. This process of system identification, when based on GP models, can play an integral part of control design in data-based control and its description as such is an essential aspect of the text. The background of GP regression is introduced first with system identification and incorporation of prior knowledge then leading into full-blown control. The book is illustrated by extensive use of examples, line drawings, and graphical presentation of computer-simulation results and plant measurements. The research results presented are applied in real-life case studies drawn from successful applications including: a gas–liquid separator control; urban-traffic signal modelling and reconstruction; and prediction of atmospheric ozone concentration. A MATLAB{\textregistered} toolbox, for identification and simulation of dynamic GP models is provided for download.}, author = {Kocijan, Ju{\v{s}}}, booktitle = {Electrical Engineering}, file = {:D$\backslash$:/my research/publication/2020/Advances in Science Technology and Engineering Systems/References/(Advances in Industrial Control) Ju{\v{s}} Kocijan (auth.)-Modelling and Control of Dynamic Systems Using Gaussian Process Models-Springer International Publishing (2016).pdf:pdf}, isbn = {9783319210209}, pages = {1--281}, title = {{Modelling and control of dynamic systems using gaussian process models}}, year = {2016} } @article{Azman2007, author = {A{\v{z}}man, K. and Kocijan, J.}, doi = {10.1016/j.isatra.2007.04.001}, journal = {ISA Transactions}, number = {4}, pages = {443--457}, pmid = {17544425}, title = {{Application of Gaussian processes for black-box modelling of biosystems}}, volume = {46}, year = {2007} } @article{Gwerder2005, author = {Gwerder, Markus and T{\"{o}}dtli, J{\"{u}}rg}, journal = {8th REHVA World Congress for Building Technologies – CLIMA}, number = {October}, pages = {1--6}, title = {{Predictive Control for Thermal Storage Management in Buildings}}, year = {2005} } @article{Andersson2019, author = {Andersson, Joel A.E. and Gillis, Joris and Horn, Greg and Rawlings, James B. and Diehl, Moritz}, doi = {10.1007/s12532-018-0139-4}, journal = {Mathematical Programming Computation}, number = {1}, title = {{CasADi: a software framework for nonlinear optimization and optimal control}}, volume = {11}, year = {2019} } @article{Abdufattokhov2020, author = {Abdufattokhov, Shokhjakhon and Ibragimova, Kamila and Gulyamova, Dilfuza and Tulaganov, Komiljon}, journal = {International Journal of Emerging Trends in Engineering Research}, title = {{Gaussian processes regression based energy system identification of manufacturing process for model predictive control}}, volume = {8}, number = {9}, year = {2020}, pages={4927--4932}, doi = {10.30534/ijeter/2020/06892020} } @inproceedings{Abdufattokhov2019Prob, author={S. {Abdufattokhov} and B. {Muhiddinov}}, booktitle={2019 International Conference on Information Science and Communications Technologies (ICISCT)}, title={Probabilistic Approach for System Identification using Machine Learning}, year={2019}, volume={}, number={}, pages={1-4}, doi={10.1109/ICISCT47635.2019.9012025} } @article{Thompson2009, author = {Thompson, Keith Russell}, journal = {Thesis}, title = {{Implementation of Gaussian Process models for Nonlinear System Identification}}, year = {2009} }