
@misc{aermecRoofTopManuelSelection,
  title = {Roof-{{Top Manuel}} de Selection et {{Installation}}},
  author = {{AERMEC}},
  file = {/home/radu/Zotero/storage/8H7BWC7Z/manuel technique RTY.PDF}
}

@article{anderssonCasADiSoftwareFramework2019,
  title = {{{CasADi}}: A Software Framework for Nonlinear Optimization and Optimal Control},
  shorttitle = {{{CasADi}}},
  author = {Andersson, Joel A. E. and Gillis, Joris and Horn, Greg and Rawlings, James B. and Diehl, Moritz},
  date = {2019-03},
  journaltitle = {Mathematical Programming Computation},
  shortjournal = {Math. Prog. Comp.},
  volume = {11},
  pages = {1--36},
  issn = {1867-2949, 1867-2957},
  doi = {10.1007/s12532-018-0139-4},
  url = {http://link.springer.com/10.1007/s12532-018-0139-4},
  urldate = {2021-06-03},
  file = {/home/radu/Zotero/storage/4HZ595JR/Andersson et al. - 2019 - CasADi a software framework for nonlinear optimiz.pdf},
  langid = {english},
  number = {1}
}

@article{arroyoPythonBasedToolboxModel2018,
  title = {A {{Python}}-{{Based Toolbox}} for {{Model Predictive Control Applied}} to {{Buildings}}},
  author = {Arroyo, Javier},
  date = {2018},
  pages = {14},
  abstract = {The use of Model Predictive Control (MPC) in Building Management Systems (BMS) has proven to outperform the traditional Rule-Based Controllers (RBC). These optimal controllers are able to minimize the energy use within building, by taking into account the weather forecast and occupancy profiles, while guaranteeing thermal comfort in the building. To this end, they anticipate the dynamic behaviour based on a mathematical model of the system. However, these MPC strategies are still not widely used in practice because a substantial engineering effort is needed to identify a tailored model for each building and Heat Ventilation and Air Conditioning (HVAC) system.},
  file = {/home/radu/Zotero/storage/UC4XY3WZ/Arroyo - 2018 - A Python-Based Toolbox for Model Predictive Contro.pdf},
  langid = {english}
}

@online{BuildingsHeatTransferData,
  title = {Buildings.{{HeatTransfer}}.{{Data}}.{{Solids}}},
  url = {https://simulationresearch.lbl.gov/modelica/releases/latest/help/Buildings_HeatTransfer_Data_Solids.html#Buildings.HeatTransfer.Data.Solids},
  urldate = {2021-06-04},
  file = {/home/radu/Zotero/storage/UWE74DAN/Buildings_HeatTransfer_Data_Solids.html}
}

@online{CARNOTManual,
  title = {{{CARNOT}} Manual},
  url = {https://www.fh-aachen.de/fileadmin/ins/ins_solar_institut/Carnot_Downloads/Manual.html},
  urldate = {2021-06-11},
  file = {/home/radu/Zotero/storage/JHEQ88EG/Manual.html}
}

@online{doubleglazingDoubleGlazingValue,
  title = {Double {{Glazing U Value Explained}}},
  author = {Doubleglazing, Admin},
  url = {https://www.doubleglazing.com/windows/double-glazing-materials/double-glazing-u-value-explained/},
  urldate = {2021-06-08},
  abstract = {What is the U value? Double glazing can be measured on its enery efficiency whilst comparing it to other windows, by using a calculation system known as the U value (or U factor). The U value can be adopted for any kind of home construction and measures how effective that component is at retaining},
  file = {/home/radu/Zotero/storage/ISYJ8QVT/double-glazing-u-value-explained.html},
  langid = {british},
  organization = {{Double Glazing.com}}
}

@online{ElevationFinder,
  title = {Elevation {{Finder}}},
  url = {https://www.freemaptools.com/elevation-finder.htm},
  urldate = {2021-06-11},
  file = {/home/radu/Zotero/storage/T9PQB6EC/elevation-finder.html}
}

@article{erbsEstimationDiffuseRadiation1982,
  title = {Estimation of the Diffuse Radiation Fraction for Hourly, Daily and Monthly-Average Global Radiation},
  author = {Erbs, D. G. and Klein, S. A. and Duffie, J. A.},
  date = {1982-01-01},
  journaltitle = {Solar Energy},
  shortjournal = {Solar Energy},
  volume = {28},
  pages = {293--302},
  issn = {0038-092X},
  doi = {10.1016/0038-092X(82)90302-4},
  url = {https://www.sciencedirect.com/science/article/pii/0038092X82903024},
  urldate = {2021-06-11},
  abstract = {Hourly pyrheliometer and pyranometer data from four U.S. locations are used to establish a relationship between the hourly diffuse fraction and the hourly clearness index kT. This relationship is compared to the relationship established by Orgill and Hollands and to a set of data from Highett, Australia, and agreement is within a few percent in both cases. The transient simulation program TRNSYS is used to calculate the annual performance of solar energy systems using several correlations. For the systems investigated, the effect of simulating the random distribution of the hourly diffuse fraction is negligible. A seasonally dependent daily diffuse correlation is developed from the data, and this daily relationship is used to derive a correlation for the monthly-average diffuse fraction.},
  file = {/home/radu/Zotero/storage/BN2KS23L/0038092X82903024.html},
  langid = {english},
  number = {4}
}

@book{europeancommission.jointresearchcentre.EnergyConsumptionEnergy2018,
  title = {Energy Consumption and Energy Efficiency Trends in the {{EU}}-28 for the Period 2000-2016.},
  author = {{European Commission. Joint Research Centre.}},
  date = {2018},
  publisher = {{Publications Office}},
  location = {{LU}},
  url = {https://data.europa.eu/doi/10.2760/574824},
  urldate = {2021-06-25},
  file = {/home/radu/Zotero/storage/6WCHIEUC/European Commission. Joint Research Centre. - 2018 - Energy consumption and energy efficiency trends in.pdf},
  langid = {english}
}

@article{f.holmgrenPvlibPythonPython2018,
  title = {Pvlib Python: A Python Package for Modeling Solar Energy Systems},
  shorttitle = {Pvlib Python},
  author = {F. Holmgren, William and W. Hansen, Clifford and A. Mikofski, Mark},
  date = {2018-09-07},
  journaltitle = {Journal of Open Source Software},
  shortjournal = {JOSS},
  volume = {3},
  pages = {884},
  issn = {2475-9066},
  doi = {10.21105/joss.00884},
  url = {http://joss.theoj.org/papers/10.21105/joss.00884},
  urldate = {2021-06-11},
  file = {/home/radu/Zotero/storage/DGWSAEYK/F. Holmgren et al. - 2018 - pvlib python a python package for modeling solar .pdf},
  number = {29}
}

@article{fabiettiMultitimeScaleCoordination2018,
  title = {Multi-Time Scale Coordination of Complementary Resources for the Provision of Ancillary Services},
  author = {Fabietti, Luca and Qureshi, Faran A. and Gorecki, Tomasz T. and Salzmann, Christophe and Jones, Colin N.},
  date = {2018-11},
  journaltitle = {Applied Energy},
  shortjournal = {Applied Energy},
  volume = {229},
  pages = {1164--1180},
  issn = {03062619},
  doi = {10.1016/j.apenergy.2018.08.045},
  url = {https://linkinghub.elsevier.com/retrieve/pii/S0306261918312005},
  urldate = {2019-06-09},
  abstract = {This paper presents a predictive control scheme for coordinating a set of heterogeneous and complementary resources at different timescales for the provision of ancillary services. In particular, we combine building thermodynamics (slow), and energy storage systems (fast resources) to augment the flexibility that can be provided to the grid compared to the flexibility that any of these resources can provide individually. A multilevel control scheme based on data-based robust optimization methods is developed that enables heterogeneous resources at different time scales (slow and fast) to provide fast regulation services, especially a secondary frequency control service. A data-based predictor is developed to forecast the future regulation signal and is used to improve the performance of the controller in real-time operation. The proposed control method is used to conduct experiments, for nine consecutive days, demonstrating the provision of secondary frequency control fully complying to the Swiss regulations, using a controllable building cooling system on the EPFL campus and an emulated grid-connected energy storage system. The experimental results show that optimally combining such slow and fast resources can significantly augment the flexibility that can be provided to the grid. To the best of author’s knowledge, this work is the first experimental demonstration of coordinating heterogeneous demandresponse to provide secondary frequency control service.},
  file = {/home/radu/Zotero/storage/HX6FBTNX/Fabietti et al. - 2018 - Multi-time scale coordination of complementary res.pdf},
  langid = {english}
}

@article{garrido-merchanDealingCategoricalIntegervalued2020,
  title = {Dealing with {{Categorical}} and {{Integer}}-Valued {{Variables}} in {{Bayesian Optimization}} with {{Gaussian Processes}}},
  author = {Garrido-Merchán, Eduardo C. and Hernández-Lobato, Daniel},
  date = {2020-03},
  journaltitle = {Neurocomputing},
  shortjournal = {Neurocomputing},
  volume = {380},
  pages = {20--35},
  issn = {09252312},
  doi = {10.1016/j.neucom.2019.11.004},
  url = {http://arxiv.org/abs/1805.03463},
  urldate = {2021-06-03},
  abstract = {Bayesian Optimization (BO) is useful for optimizing functions that are expensive to evaluate, lack an analytical expression and whose evaluations can be contaminated by noise. These methods rely on a probabilistic model of the objective function, typically a Gaussian process (GP), upon which an acquisition function is built. The acquisition function guides the optimization process and measures the expected utility of performing an evaluation of the objective at a new point. GPs assume continuous input variables. When this is not the case, for example when some of the input variables take categorical or integer values, one has to introduce extra approximations. Consider a suggested input location taking values in the real line. Before doing the evaluation of the objective, a common approach is to use a one hot encoding approximation for categorical variables, or to round to the closest integer, in the case of integer-valued variables. We show that this can lead to optimization problems and describe a more principled approach to account for input variables that are categorical or integer-valued. We illustrate in both synthetic and a real experiments the utility of our approach, which significantly improves the results of standard BO methods using Gaussian processes on problems with categorical or integer-valued variables.},
  archiveprefix = {arXiv},
  eprint = {1805.03463},
  eprinttype = {arxiv},
  file = {/home/radu/Zotero/storage/UQSNGFSC/Garrido-Merchán and Hernández-Lobato - 2020 - Dealing with Categorical and Integer-valued Variab.pdf},
  keywords = {Computer Science - Machine Learning,Statistics - Machine Learning},
  langid = {english}
}

@online{glassforeuropeMinimumPerformanceRequirements2018,
  title = {Minimum {{Performance Requirements}} for {{Windows}}},
  author = {{Glass For Europe}},
  date = {2018-12-19T09:45:24+00:00},
  url = {https://glassforeurope.com/minimum-performance-requirements-for-windows/},
  urldate = {2021-06-08},
  abstract = {Minimum performance requirements~for window replacement in the~residential sector ~ As required by the Energy Performance of Buildings Directive, EU Member States have to set cost-optimal minimum energy performance requirements for the replacement of building elements such as windows. The map and the table below illustrate how EU Member States have implemented this 2012 EPBD requirement},
  file = {/home/radu/Zotero/storage/XK6LLKBM/minimum-performance-requirements-for-windows.html},
  langid = {american},
  organization = {{Glass for Europe}}
}

@online{GoogleMaps,
  title = {Google {{Maps}}},
  url = {https://www.google.com/maps/place/46%C2%B031'17.4%22N+6%C2%B034'08.3%22E/@46.5214889,6.5688311,21z/data=!4m6!3m5!1s0x478c3101c29a8bf5:0x628d91034302f8be!7e2!8m2!3d46.5214889!4d6.5689679},
  urldate = {2021-06-04},
  abstract = {Find local businesses, view maps and get driving directions in Google Maps.},
  file = {/home/radu/Zotero/storage/9M2GL5H6/data=!4m6!3m5!1s0x478c3101c29a8bf50x628d91034302f8be!7e2!8m2!3d46.5214889!4d6.html},
  langid = {english},
  organization = {{Google Maps}}
}

@article{GuideEnergyEfficientWindows,
  title = {Guide to {{Energy}}-{{Efficient Windows}}},
  journaltitle = {US Department of Energy},
  pages = {2},
  file = {/home/radu/Zotero/storage/UEKFZDDC/Guide to Energy-Efficient Windows.pdf},
  langid = {english}
}

@inproceedings{hensman2014scalable,
  title = {Scalable Variational Gaussian Process Classification},
  booktitle = {Proceedings of {{AISTATS}}},
  author = {Hensman, James and Matthews, Alexander G. de G. and Ghahramani, Zoubin},
  date = {2015}
}

@online{hensmanGaussianProcessesBig2013,
  title = {Gaussian {{Processes}} for {{Big Data}}},
  author = {Hensman, James and Fusi, Nicolo and Lawrence, Neil D.},
  date = {2013-09-26},
  url = {http://arxiv.org/abs/1309.6835},
  urldate = {2021-06-15},
  abstract = {We introduce stochastic variational inference for Gaussian process models. This enables the application of Gaussian process (GP) models to data sets containing millions of data points. We show how GPs can be vari- ationally decomposed to depend on a set of globally relevant inducing variables which factorize the model in the necessary manner to perform variational inference. Our ap- proach is readily extended to models with non-Gaussian likelihoods and latent variable models based around Gaussian processes. We demonstrate the approach on a simple toy problem and two real world data sets.},
  archiveprefix = {arXiv},
  eprint = {1309.6835},
  eprinttype = {arxiv},
  file = {/home/radu/Zotero/storage/HALCX455/Hensman et al. - 2013 - Gaussian Processes for Big Data.pdf;/home/radu/Zotero/storage/KNNUVW7T/1309.html},
  keywords = {Computer Science - Machine Learning,Statistics - Machine Learning},
  primaryclass = {cs, stat}
}

@software{holmgrenPvlibPvlibpythonV02021,
  title = {Pvlib/Pvlib-Python: V0.8.1},
  shorttitle = {Pvlib/Pvlib-Python},
  author = {Holmgren, Will and Calama-Consulting and Hansen, Cliff and Mikofski, Mark and Anderson, Kevin and Lorenzo, Tony and Krien, Uwe and Bmu and Stark, Cameron and DaCoEx and Driesse, Anton and Konstant\_t and Mayudong and Peque, Miguel Sánchez De León and Heliolytics and Miller, Ed and Anoma, Marc A. and Guo, Veronica and Boeman, Leland and Jforbess and Lunel, Tanguy and Morgan, Alexander and Stein, Joshua and Leroy, Cedric and Ahan M R and JPalakapillyKWH and Dollinger, Johannes and Anderson, Kevin and MLEEFS and Dowson, Oscar},
  date = {2021-01-05},
  doi = {10.5281/ZENODO.4417742},
  url = {https://zenodo.org/record/4417742},
  urldate = {2021-06-11},
  abstract = {https://pvlib-python.readthedocs.io/en/v0.8.1/whatsnew.html},
  organization = {{Zenodo}},
  version = {v0.8.1}
}

@online{HSLCollectionFortran,
  title = {{{HSL}}, a Collection of {{Fortran}} Codes for Large-Scale Scientific Computation.     {{See}} {{http://www.hsl.rl.ac.uk/}}},
  url = {https://www.hsl.rl.ac.uk/},
  urldate = {2021-06-03},
  file = {/home/radu/Zotero/storage/AU982UBT/www.hsl.rl.ac.uk.html}
}

@inproceedings{jainLearningControlUsing2018,
  title = {Learning and {{Control Using Gaussian Processes}}},
  booktitle = {2018 {{ACM}}/{{IEEE}} 9th {{International Conference}} on {{Cyber}}-{{Physical Systems}} ({{ICCPS}})},
  author = {Jain, Achin and Nghiem, Truong and Morari, Manfred and Mangharam, Rahul},
  date = {2018-04},
  pages = {140--149},
  publisher = {{IEEE}},
  location = {{Porto}},
  doi = {10.1109/ICCPS.2018.00022},
  url = {https://ieeexplore.ieee.org/document/8443729/},
  urldate = {2021-06-03},
  abstract = {Building physics-based models of complex physical systems like buildings and chemical plants is extremely cost and time prohibitive for applications such as real-time optimal control, production planning and supply chain logistics. Machine learning algorithms can reduce this cost and time complexity, and are, consequently, more scalable for large-scale physical systems. However, there are many practical challenges that must be addressed before employing machine learning for closed-loop control. This paper proposes the use of Gaussian Processes (GP) for learning control-oriented models: (1) We develop methods for the optimal experiment design (OED) of functional tests to learn models of a physical system, subject to stringent operational constraints and limited availability of the system. Using a Bayesian approach with GP, our methods seek to select the most informative data for optimally updating an existing model. (2) We also show that black-box GP models can be used for receding horizon optimal control with probabilistic guarantees on constraint satisfaction through chance constraints. (3) We further propose an online method for continuously improving the GP model in closed-loop with a real-time controller. Our methods are demonstrated and validated in a case study of building energy control and Demand Response.},
  eventtitle = {2018 {{ACM}}/{{IEEE}} 9th {{International Conference}} on {{Cyber}}-{{Physical Systems}} ({{ICCPS}})},
  file = {/home/radu/Zotero/storage/89X86H5F/Jain et al. - 2018 - Learning and Control Using Gaussian Processes.pdf},
  isbn = {978-1-5386-5301-2},
  langid = {english}
}

@article{johraNumericalAnalysisImpact2017,
  title = {Numerical {{Analysis}} of the {{Impact}} of {{Thermal Inertia}} from the {{Furniture}} / {{Indoor Content}} and {{Phase Change Materials}} on the {{Building Energy Flexibility}}},
  author = {Johra, Hicham and Heiselberg, Per Kvols and Dréau, Jérôme Le},
  date = {2017},
  pages = {8},
  abstract = {Many numerical models for building energy simulation assume empty rooms and do not account for the indoor content of occupied buildings. Furnishing elements and indoor items have complicated shapes and are made of various materials. Therefore, most of the people prefer to ignore them. However, this simplification can be problematic for accurate calculation of the transient indoor temperature. This article firstly reviews different solutions to include the indoor content in building models and suggests typical values for its characteristics. Secondly, the paper presents the results of a numerical study investigating the influence of the different types of thermal inertia on buildings energy flexibility. Although the insulation level and thermal mass of a building envelope are the dominant parameters, it appears that indoor content cannot be neglected for lightweight structure building simulations. Finally, it is shown that the integration of phase change materials in wallboards or furniture elements can appreciably improve the energy flexibility of buildings.},
  file = {/home/radu/Zotero/storage/9KFSNVNW/Johra et al. - 2017 - Numerical Analysis of the Impact of Thermal Inerti.pdf},
  langid = {english}
}

@article{kabzanLearningBasedModelPredictive2019,
  title = {Learning-{{Based Model Predictive Control}} for {{Autonomous Racing}}},
  author = {Kabzan, J. and Hewing, Lukas and Liniger, Alexander and Zeilinger, Melanie N.},
  date = {2019-10},
  journaltitle = {IEEE Robotics and Automation Letters},
  volume = {4},
  pages = {3363--3370},
  issn = {2377-3766},
  doi = {10.1109/LRA.2019.2926677},
  abstract = {In this letter, we present a learning-based control approach for autonomous racing with an application to the AMZ Driverless race car gotthard. One major issue in autonomous racing is that accurate vehicle models that cover the entire performance envelope of a race car are highly nonlinear, complex, and complicated to identify, rendering them impractical for control. To address this issue, we employ a relatively simple nominal vehicle model, which is improved based on measurement data and tools from machine learning.The resulting formulation is an online learning data-driven model predictive controller, which uses Gaussian processes regression to take residual model uncertainty into account and achieve safe driving behavior. To improve the vehicle model online, we select from a constant in-flow of data points according to a criterion reflecting the information gain, and maintain a small dictionary of 300 data points. The framework is tested on the full-size AMZ Driverless race car, where it is able to improve the vehicle model and reduce lap times by \$ \textbackslash mathbf10\%\$ while maintaining safety of the vehicle.},
  eventtitle = {{{IEEE Robotics}} and {{Automation Letters}}},
  file = {/home/radu/Zotero/storage/CUJ9MGH7/Kabzan et al. - 2019 - Learning-Based Model Predictive Control for Autono.pdf;/home/radu/Zotero/storage/77YZEZQB/8754713.html},
  keywords = {Adaptive systems,autonomous racing,Autonomous vehicles,learning and adaptive systems,Learning systems,Model learning for control,model predictive control,Predictive control,Vehicle dynamics},
  number = {4}
}

@online{KernelCookbooka,
  title = {Kernel {{Cookbook}}},
  url = {https://www.cs.toronto.edu/~duvenaud/cookbook/},
  urldate = {2021-06-15},
  file = {/home/radu/Zotero/storage/LTPBRTNG/cookbook.html}
}

@software{kimballGIMPGNUImage,
  title = {{{GIMP}}: {{GNU Image Manipulation Program}}},
  author = {Kimball, Spencer and Mattis, Peter and {The GIMP Development Team}},
  url = {https://www.gimp.org},
  version = {2.10.24}
}

@book{kocijanModellingControlDynamic2016,
  title = {Modelling and {{Control}} of {{Dynamic Systems Using Gaussian Process Models}}},
  author = {Kocijan, Juš},
  date = {2016},
  publisher = {{Springer International Publishing}},
  location = {{Cham}},
  doi = {10.1007/978-3-319-21021-6},
  url = {http://link.springer.com/10.1007/978-3-319-21021-6},
  urldate = {2019-06-10},
  file = {/home/radu/Zotero/storage/YW9S25QI/Kocijan - 2016 - Modelling and Control of Dynamic Systems Using Gau.pdf},
  isbn = {978-3-319-21020-9 978-3-319-21021-6},
  series = {Advances in {{Industrial Control}}}
}

@article{liuExperimentalAnalysisSimulated2006,
  title = {Experimental Analysis of Simulated Reinforcement Learning Control for Active and Passive Building Thermal Storage Inventory: {{Part}} 2: {{Results}} and Analysis},
  shorttitle = {Experimental Analysis of Simulated Reinforcement Learning Control for Active and Passive Building Thermal Storage Inventory},
  author = {Liu, Simeng and Henze, Gregor P.},
  date = {2006-02-01},
  journaltitle = {Energy and Buildings},
  shortjournal = {Energy and Buildings},
  volume = {38},
  pages = {148--161},
  issn = {0378-7788},
  doi = {10.1016/j.enbuild.2005.06.001},
  url = {https://www.sciencedirect.com/science/article/pii/S0378778805000861},
  urldate = {2021-06-20},
  abstract = {This paper is the second part of a two-part investigation of a novel approach to optimally control commercial building passive and active thermal storage inventory. The proposed building control approach is based on simulated reinforcement learning, which is a hybrid control scheme that combines features of model-based optimal control and model-free learning control. An experimental study was carried out to analyze the performance of a hybrid controller installed in a full-scale laboratory facility. The first paper introduced the theoretical foundation of this investigation including the fundamental theory of reinforcement learning control. This companion paper presents a discussion and analysis of the experiment results. The results confirm the feasibility of the proposed control approach. Operating cost savings were attained with the proposed control approach compared with conventional building control; however, the savings are lower than for the case of model-based predictive optimal control As for the case of model-based predictive control, the performance of the hybrid controller is largely affected by the quality of the training model, and extensive real-time learning is required for the learning controller to eliminate any false cues it receives during the initial training period. Nevertheless, compared with standard reinforcement learning, the proposed hybrid controller is much more readily implemented in a commercial building.},
  file = {/home/radu/Zotero/storage/S7QXQJVH/Liu and Henze - 2006 - Experimental analysis of simulated reinforcement l.pdf;/home/radu/Zotero/storage/I3GBEBHA/S0378778805000861.html},
  keywords = {Learning control,Load shifting,Optimal control,Reinforcement learning,Thermal Energy Storage (TES)},
  langid = {english},
  number = {2}
}

@article{liuUnderstandingComparingScalable2019,
  title = {Understanding and Comparing Scalable {{Gaussian}} Process Regression for Big Data},
  author = {Liu, Haitao and Cai, Jianfei and Ong, Yew-Soon and Wang, Yi},
  date = {2019-01},
  journaltitle = {Knowledge-Based Systems},
  shortjournal = {Knowledge-Based Systems},
  volume = {164},
  pages = {324--335},
  issn = {09507051},
  doi = {10.1016/j.knosys.2018.11.002},
  url = {http://linkinghub.elsevier.com/retrieve/pii/S0950705118305380},
  urldate = {2021-06-15},
  file = {/home/radu/Zotero/storage/B3I5KT7W/0307ea49eae1cbe24736d3e77e33fcd8.pdf;/home/radu/Zotero/storage/V6BDDI6C/Liu et al. - 2019 - Understanding and comparing scalable Gaussian proc.pdf},
  langid = {english}
}

@article{lohmannEinfuehrungSoftwareMATLAB,
  title = {Einführung in die Software MATLAB® - Simulink® und die Toolboxen CARNOT und Stateflow® zur Simulation von Gebäude- und Heizungstechnik},
  author = {Lohmann, Sandra},
  pages = {43},
  file = {/home/radu/Zotero/storage/EXHDUWYA/Lohmann - Einführung in die Software MATLAB® - Simulink® und.pdf},
  langid = {german}
}

@article{massagrayThermalBuildingModelling2016,
  title = {Thermal Building Modelling Using {{Gaussian}} Processes},
  author = {Massa Gray, Francesco and Schmidt, Michael},
  date = {2016-05-01},
  journaltitle = {Energy and Buildings},
  shortjournal = {Energy and Buildings},
  volume = {119},
  pages = {119--128},
  issn = {0378-7788},
  doi = {10.1016/j.enbuild.2016.02.004},
  url = {sciencedirect.com/science/article/pii/S0378778816300494},
  urldate = {2021-06-19},
  abstract = {This paper analyzes the suitability of Gaussian processes for thermal building modelling by comparing the day-ahead prediction error of the internal air temperature with a grey-box model. The reference building is a single-zone office with a hydronic heating system, modelled in TRNSYS and simulated during the winter and spring periods. Using the output data of the reference building, the parameters of a Gaussian process and of a physics-based grey-box model are identified, with training periods ranging from three days to six weeks. After three weeks of training, the Gaussian processes achieve 27\% lower prediction errors during occupied times compared to the grey-box model. During unoccupied times, however, the Gaussian processes perform consistently worse than the grey-box model. This is due to their large generalization error, especially when faced with untrained ambient temperature values. To reduce the impact of changing weather conditions, adaptive training is applied to the Gaussian processes. When re-training the models every 24h, the prediction error is reduced over 21\% during unoccupied times and over 10\% during occupied times compared to the non-adaptive training case. These results show that the proposed Gaussian process model can correctly describe a building's thermal dynamics. However, in its current form the model is limited to applications where the prediction during occupied times is more relevant.},
  file = {/home/radu/Zotero/storage/EQNPYPBA/Massa Gray and Schmidt - 2016 - Thermal building modelling using Gaussian processe.pdf},
  keywords = {Building modelling,Gaussian processes,GP,Grey-box,HVAC,Simulation},
  langid = {english}
}

@article{matthewsGPflowGaussianProcess2017,
  title = {{{GPflow}}: {{A Gaussian Process Library}} Using {{TensorFlow}}},
  shorttitle = {{{GPflow}}},
  author = {Matthews, Alexander G. de G. and van der Wilk, Mark and Nickson, Tom and Fujii, Keisuke and Boukouvalas, Alexis and Le\{\textbackslash 'o\}n-Villagr\{\textbackslash 'a\}, Pablo and Ghahramani, Zoubin and Hensman, James},
  date = {2017},
  journaltitle = {Journal of Machine Learning Research},
  volume = {18},
  pages = {1--6},
  issn = {1533-7928},
  url = {http://jmlr.org/papers/v18/16-537.html},
  urldate = {2021-06-03},
  file = {/home/radu/Zotero/storage/VI8IWMWL/Matthews et al. - 2017 - GPflow A Gaussian Process Library using TensorFlo.pdf;/home/radu/Zotero/storage/S6WSAX5Z/16-537.html},
  number = {40}
}

@article{nattererModelingMultilayerBeam2008,
  title = {Modeling of Multi-Layer Beam with Interlayer Slips},
  author = {Natterer, Johannes and Weinand, Yves},
  date = {2008-01-01},
  journaltitle = {Structures and Architecture - Proceedings of the 1st International Conference on Structures and Architecture, ICSA 2010},
  shortjournal = {Structures and Architecture - Proceedings of the 1st International Conference on Structures and Architecture, ICSA 2010},
  volume = {4},
  issn = {978-0-415-49249-2},
  doi = {10.1201/b10428-152},
  abstract = {In the early 90th the IBOIS-EPFL developed a new kind of shell structure. The ribs were made with simple planks which are waved together to build a spacial ribbed shell. The first application was the Polydome in 1993, and the most fascinating has been the Expodach in Hannover in year 2000 [1]. However, the calculation and the realization of such structures requires particular knowledge and experience. That is’s why the construction of such structures is something exceptional. In addition to the anisotropy of material wood, the spatial structure of laminated and screwed beams has a structural anisotropy. They constitute highly unspecified static systems. Currently the engineer does not have any effective method to calculate these kinds of spatial structures, made out of curved screwed lamellate boards. The existing approximations for complex curved structures are not satisfying. The main differences are noticed especially upon the analysis of the stability of structures subject to horizontal loads. The following article will compare a 6-layered beam with inter-layer slips in different load cases and situations. The beam is composed of 6 planks with a section of 140/27mm. The connections between the layers are screws. The studied parameters are the distance between the connector, the length of the beam and 3 different load cases. A total of 24 elements have been tested and compared between the laboratory test and different theories. A very important parameter is the stiffness of the connector. Thus additional tests have been made to simulate the stiffness of the screws used. A bi-exponential law has been generated to be used in the theoretical evaluation of the tests. The actual theories which have been used to simulate the behavior of the sample are gamma- Method of Möhler-Schelling, Appendix F of the Germand standard E- DIN 1052 of Prof. Kreuzinger, framework systems developed by Kneidl and Hartmann and finally a multiy-layer finite element developed at the LSC- EPFL by Prof. Frei and Dr. Krawczyk.},
  file = {/home/radu/Zotero/storage/TJE2V99Y/Natterer and Weinand - 2008 - Modeling of multi-layer beam with interlayer slips.pdf}
}

@article{nattererPolydomeTimberShell1993,
  title = {Polydôme: {{A Timber Shell}}},
  shorttitle = {Polydôme},
  author = {Natterer, Julius and MacIntyre, John},
  date = {1993-05-01},
  journaltitle = {Structural Engineering International},
  volume = {3},
  pages = {82--83},
  publisher = {{Taylor \& Francis}},
  issn = {1016-8664},
  doi = {10.2749/101686693780612376},
  url = {https://doi.org/10.2749/101686693780612376},
  urldate = {2021-06-04},
  abstract = {The “Polydôme” is a 25 m-span timber shell exhibition built to commemorate the seven hundredth anniversary of the Swiss Confederation. The project was an opportunity to present concrete evidence or research, as well as to demonstrate the possibilities of engineered timber structures using simple material means.},
  annotation = {\_eprint: https://doi.org/10.2749/101686693780612376},
  file = {/home/radu/Zotero/storage/7LIEDD9S/Natterer and MacIntyre - 1993 - Polydôme A Timber Shell, Switzerland.pdf;/home/radu/Zotero/storage/MZ3EIFC4/101686693780612376.html},
  keywords = {Decking,Polydôme (Switzerland),Roof systems,Structural analysis,Timber shells,Timber structures},
  number = {2}
}

@inproceedings{nghiemDatadrivenDemandResponse2017,
  title = {Data-Driven Demand Response Modeling and Control of Buildings with {{Gaussian Processes}}},
  booktitle = {2017 {{American Control Conference}} ({{ACC}})},
  author = {Nghiem, Truong X. and Jones, Colin N.},
  date = {2017-05},
  pages = {2919--2924},
  publisher = {{IEEE}},
  location = {{Seattle, WA, USA}},
  doi = {10.23919/ACC.2017.7963394},
  url = {http://ieeexplore.ieee.org/document/7963394/},
  urldate = {2019-06-09},
  abstract = {This paper presents an approach to provide demand response services with buildings. Each building receives a normalized signal that tells it to increase or decrease its power demand, and the building is free to implement any suitable strategy to follow the command, most likely by changing some of its setpoints. Due to this freedom, the proposed approach lowers the barrier for any buildings equipped with a reasonably functional building management system to participate in the scheme. The response of the buildings to the control signal is modeled by a Gaussian Process, which can predict the power demand of the buildings and also provide a measure of its confidence in the prediction. A battery is included in the system to compensate for this uncertainty and improve the demand response performance of the system. A model predictive controller is developed to optimally control the buildings and the battery, while ensuring their operational constraints with high probability. Our approach is validated by realistic co-simulations between Matlab and the building energy simulator EnergyPlus.},
  eventtitle = {2017 {{American Control Conference}} ({{ACC}})},
  file = {/home/radu/Zotero/storage/FHIIJQWW/Nghiem și Jones - 2017 - Data-driven demand response modeling and control o.pdf},
  isbn = {978-1-5090-5992-8},
  langid = {english}
}

@online{pinterestSphericalDomeCalculator,
  title = {Spherical {{Dome Calculator}}},
  author = {this on Pinterest, Dave South Share this via Email Share this on Twitter Share this on Facebook Share this on Reddit Share},
  url = {https://monolithicdome.com/spherical-dome-calculator},
  urldate = {2021-06-05},
  abstract = {The MDI Spherical Dome Calculator assists in calculating common design elements of a partial sphere set on an optional stem wall. It helps with quick design ideas as well as provides accurate measurements when finalizing structural elements. Outputs include surface area, volume, circumference, and distances along and around the various details of the dome design.},
  file = {/home/radu/Zotero/storage/SVQGWWW9/spherical-dome-calculator.html},
  langid = {english},
  organization = {{Monolithic Dome Institute}}
}

@inproceedings{pleweSupervisoryModelPredictive2020,
  title = {A {{Supervisory Model Predictive Control Framework}} for {{Dual Temperature Setpoint Optimization}}},
  booktitle = {2020 {{American Control Conference}} ({{ACC}})},
  author = {Plewe, Kaden E. and Smith, Amanda D. and Liu, Mingxi},
  date = {2020-07},
  pages = {1900--1906},
  issn = {2378-5861},
  doi = {10.23919/ACC45564.2020.9147308},
  abstract = {In this paper, a model predictive control (MPC) framework for building energy system setpoint optimization is developed and tested. The performance of the MPC framework is presented in comparison to a baseline case, where a fixed setpoint schedule is used. To simulate the MPC procedure, an EnergyPlus building model is used to represent the actual building that the optimal setpoints are applied to, and a Gaussian process (GP) regression meta-model is used in the MPC procedure that generates the optimal setpoints. The performance outputs that are used for evaluation are total heating, ventilation and air conditioning (HVAC) energy usage and the Fanger predicted mean vote (PMV) thermal comfort measure. The inputs for the GP regression meta-models are selected to be representative of data points that could be obtained by modern supervisory control and data acquisition (SCADA) systems to support data-driven building models. The supervisory MPC framework is capable of reducing the total energy usage with minor adjustments in thermal comfort.},
  eventtitle = {2020 {{American Control Conference}} ({{ACC}})},
  keywords = {Atmospheric modeling,Buildings,Cooling,Data models,Heating systems,Mathematical model,Predictive models}
}

@book{rasmussenGaussianProcessesMachine2006,
  title = {Gaussian Processes for Machine Learning},
  author = {Rasmussen, Carl Edward and Williams, Christopher K. I.},
  date = {2006},
  publisher = {{MIT Press}},
  location = {{Cambridge, Mass}},
  annotation = {OCLC: ocm61285753},
  file = {/home/radu/Zotero/storage/VBZDYFA6/Rasmussen and Williams - 2006 - Gaussian processes for machine learning.pdf},
  isbn = {978-0-262-18253-9},
  keywords = {Data processing,Gaussian processes,Machine learning,Mathematical models},
  langid = {english},
  pagetotal = {248},
  series = {Adaptive Computation and Machine Learning}
}

@article{sia180:2014ProtectionThermiqueProtection2014,
  title = {Protection Thermique, Protection Contre l'humidité et Climat Intérieur Dans Les Bâtiments},
  author = {{SIA 180:2014}},
  date = {2014},
  journaltitle = {Schweizerischer Ingenieur- und Architektenverein, Zürich, CH}
}

@misc{tensorflow2015-whitepaper,
  title = {{{TensorFlow}}: {{Large}}-Scale Machine Learning on Heterogeneous Systems},
  author = {Abadi, Martín and Agarwal, Ashish and Barham, Paul and Brevdo, Eugene and Chen, Zhifeng and Citro, Craig and Corrado, Greg S. and Davis, Andy and Dean, Jeffrey and Devin, Matthieu and Ghemawat, Sanjay and Goodfellow, Ian and Harp, Andrew and Irving, Geoffrey and Isard, Michael and Jia, Yangqing and Jozefowicz, Rafal and Kaiser, Lukasz and Kudlur, Manjunath and Levenberg, Josh and Mané, Dandelion and Monga, Rajat and Moore, Sherry and Murray, Derek and Olah, Chris and Schuster, Mike and Shlens, Jonathon and Steiner, Benoit and Sutskever, Ilya and Talwar, Kunal and Tucker, Paul and Vanhoucke, Vincent and Vasudevan, Vijay and Viégas, Fernanda and Vinyals, Oriol and Warden, Pete and Wattenberg, Martin and Wicke, Martin and Yu, Yuan and Zheng, Xiaoqiang},
  date = {2015},
  url = {https://www.tensorflow.org/}
}

@article{tsemekiditzeiranakiAnalysisEUResidential2019,
  title = {Analysis of the {{EU Residential Energy Consumption}}: {{Trends}} and {{Determinants}}},
  shorttitle = {Analysis of the {{EU Residential Energy Consumption}}},
  author = {Tsemekidi Tzeiranaki, Sofia and Bertoldi, Paolo and Diluiso, Francesca and Castellazzi, Luca and Economidou, Marina and Labanca, Nicola and Ribeiro Serrenho, Tiago and Zangheri, Paolo},
  date = {2019-01},
  journaltitle = {Energies},
  volume = {12},
  pages = {1065},
  publisher = {{Multidisciplinary Digital Publishing Institute}},
  doi = {10.3390/en12061065},
  url = {https://www.mdpi.com/1996-1073/12/6/1065},
  urldate = {2021-06-25},
  abstract = {This article analyses the status and trends of the European Union (EU) residential energy consumption in light of the energy consumption targets set by the EU 2020 and 2030 energy and climate strategies. It assesses the energy efficiency progress from 2000 to 2016, using the official Eurostat data. In 2016, the residential energy consumption amounted to 25.71\% of the EU\&rsquo;s final energy consumption, representing the second largest consuming sector after transport. Consumption-related data are discussed together with data on some main energy efficiency policies and energy consumption determinants, such as economic and population growth, weather conditions, and household and building characteristics. Indicators are identified to show the impact of specific determinants on energy consumption and a new indicator is proposed, drawing a closer link between energy trends and policy and technological changes in the sector. The analysis of these determinants highlights the complex dynamics behind the demand of energy in the residential sector. Decomposition analysis is carried out using the Logarithmic Mean Divisia Index technique to provide a more complete picture of the impact of various determinants (population, wealth, intensity, and weather) on the latest EU-28 residential energy consumption trends. The article provides a better understanding of the EU residential energy consumption, its drivers, the impact of current policies, and recommendations on future policies.},
  file = {/home/radu/Zotero/storage/JFTJ9Z9Q/Tsemekidi Tzeiranaki et al. - 2019 - Analysis of the EU Residential Energy Consumption.pdf;/home/radu/Zotero/storage/RS4MYQ3A/1065.html},
  issue = {6},
  keywords = {energy efficiency policies,indicators,regression analysis,residential energy consumption},
  langid = {english},
  number = {6}
}

@online{WhatAreTypical2018,
  title = {What Are Typical {{U}}-{{Values}} on Windows and Doors? | {{Aspire Bifolds Surrey}}},
  shorttitle = {What Are Typical {{U}}-{{Values}} on Windows and Doors?},
  date = {2018-03-15T01:49:11+00:00},
  url = {https://aspirebifolds.co.uk/2018/03/what-are-typical-u-values-on-windows-and-doors/},
  urldate = {2021-06-08},
  abstract = {What are the typical U-Values on windows you can expect with modern aluminium and PVCu windows? A guide to the expected U-Values on windows for the home.},
  file = {/home/radu/Zotero/storage/RFA838X8/what-are-typical-u-values-on-windows-and-doors.html},
  langid = {british},
  organization = {{Aspire Bifolds - Home Improvements Surrey}}
}

@online{WindowsHighperformanceCommercial2020,
  title = {Windows for {{High}}-Performance {{Commercial Buildings}}},
  date = {2020-10-31},
  url = {http://web.archive.org/web/20201031043434/https://www.commercialwindows.org/transmittance.php},
  urldate = {2021-06-08},
  file = {/home/radu/Zotero/storage/SRLH9ZPX/transmittance.html}
}

@article{yangUnderstandingVariationalLower,
  title = {Understanding the {{Variational Lower Bound}}},
  author = {Yang, Xitong},
  pages = {4},
  file = {/home/radu/Zotero/storage/C4GB9B44/Yang - Understanding the Variational Lower Bound.pdf},
  langid = {english}
}

@online{yiSparseVariationalGaussian2021,
  title = {Sparse and {{Variational Gaussian Process}} — {{What To Do When Data}} Is {{Large}}},
  author = {Yi, Wei},
  date = {2021-05-26T08:48:06},
  url = {https://towardsdatascience.com/sparse-and-variational-gaussian-process-what-to-do-when-data-is-large-2d3959f430e7},
  urldate = {2021-06-10},
  abstract = {Learn how the Sparse and Variational Gaussian Process model uses inducing variables to scale to large datasets.},
  file = {/home/radu/Zotero/storage/BIWB8N2V/sparse-and-variational-gaussian-process-what-to-do-when-data-is-large-2d3959f430e7.html},
  langid = {english},
  organization = {{Medium}}
}

@online{zengAdaptiveMPCScheme2021,
  title = {An Adaptive {{MPC}} Scheme for Energy-Efficient Control of Building {{HVAC}} Systems},
  author = {Zeng, Tingting and Barooah, Prabir},
  date = {2021-02-07},
  url = {http://arxiv.org/abs/2102.03856},
  urldate = {2021-06-20},
  abstract = {An autonomous adaptive MPC architecture is presented for control of heating, ventilation and air condition (HVAC) systems to maintain indoor temperature while reducing energy use. Although equipment use and occupant changes with time, existing MPC methods are not capable of automatically relearning models and computing control decisions reliably for extended periods without intervention from a human expert. We seek to address this weakness. Two major features are embedded in the proposed architecture to enable autonomy: (i) a system identification algorithm from our prior work that periodically re-learns building dynamics and unmeasured internal heat loads from data without requiring re-tuning by experts. The estimated model is guaranteed to be stable and has desirable physical properties irrespective of the data; (ii) an MPC planner with a convex approximation of the original nonconvex problem. The planner uses a descent and convergent method, with the underlying optimization problem being feasible and convex. A year long simulation with a realistic plant shows that both of the features of the proposed architecture - periodic model and disturbance update and convexification of the planning problem - are essential to get the performance improvement over a commonly used baseline controller. Without these features, though MPC can outperform the baseline controller in certain situations, the benefits may not be substantial enough to warrant the investment in MPC.},
  archiveprefix = {arXiv},
  eprint = {2102.03856},
  eprinttype = {arxiv},
  file = {/home/radu/Zotero/storage/5DGTWGXU/Zeng and Barooah - 2021 - An adaptive MPC scheme for energy-efficient contro.pdf;/home/radu/Zotero/storage/TYEAZ4EJ/2102.html},
  keywords = {Electrical Engineering and Systems Science - Systems and Control},
  primaryclass = {cs, eess},
  version = {1}
}

@inproceedings{zengAutonomousMPCScheme2020,
  title = {An Autonomous {{MPC}} Scheme for Energy-Efficient Control of Building {{HVAC}} Systems},
  booktitle = {2020 {{American Control Conference}} ({{ACC}})},
  author = {Zeng, Tingting and Barooah, Prabir},
  date = {2020-07},
  pages = {4213--4218},
  issn = {2378-5861},
  doi = {10.23919/ACC45564.2020.9147753},
  abstract = {Model Predictive Control (MPC) is a promising technique for energy efficient control of Heating, Ventilation, and Air Conditioning (HVAC) systems. However, the need for human involvement limits current MPC strategies from widespread deployment, since (i) model identification algorithms require re-tuning of hyper-parameters, and (ii) optimizers may fail to converge within the available control computation time, or get stuck in a local minimum. In this work we propose an autonomous MPC scheme to overcome these issues. Two major features are embedded in this architecture to enable autonomy: (i) a convex identification algorithm with adaptation to time-varying building dynamics, and (ii) a convex optimizer. The model identification algorithm re-runs periodically so as to handle changes in the building's dynamics. The estimated model is guaranteed to be stable and has desirable physical properties. The optimizer uses a descent and convergent algorithm, with the underlying optimization problem being feasible and convex. Numerical results show that the proposed convex formulation is more reliable in control computation compared to the nonconvex one, and the proposed autonomous MPC architecture reduces energy consumption significantly over a conventional controller.},
  eventtitle = {2020 {{American Control Conference}} ({{ACC}})},
  keywords = {Adaptation models,Architecture,Atmospheric modeling,Buildings,Computational modeling,Computer architecture,Heuristic algorithms}
}


