References#
Kadir Amasyali and Nora M El-Gohary. A review of data-driven building energy consumption prediction studies. Renewable and Sustainable Energy Reviews, 81:1192–1205, 2018.
Christophe Andrieu, Arnaud Doucet, and Roman Holenstein. Particle markov chain monte carlo methods. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 72(3):269–342, 2010.
James V Beck, Ben Blackwell, and Charles R St Clair Jr. Inverse heat conduction: Ill-posed problems. James Beck, 1985.
Michael Betancourt. A conceptual introduction to hamiltonian monte carlo. arXiv preprint arXiv:1701.02434, 2017.
Henk AP Blom and Yaakov Bar-Shalom. The interacting multiple model algorithm for systems with markovian switching coefficients. IEEE transactions on Automatic Control, 33(8):780–783, 1988.
Luis M Candanedo, Véronique Feldheim, and Dominique Deramaix. A methodology based on hidden markov models for occupancy detection and a case study in a low energy residential building. Energy and Buildings, 148:327–341, 2017.
Olivier Cappé, Simon J Godsill, and Eric Moulines. An overview of existing methods and recent advances in sequential monte carlo. Proceedings of the IEEE, 95(5):899–924, 2007.
Herman Carstens, Xiaohua Xia, and Sarma Yadavalli. Bayesian energy measurement and verification analysis. Energies, 11(2):380, 2018.
Zhenghua Chen, Chaoyang Jiang, and Lihua Xie. Building occupancy estimation and detection: a review. Energy and Buildings, 169:260–270, 2018.
Adrian Chong, Godfried Augenbroe, and Da Yan. Occupancy data at different spatial resolutions: building energy performance and model calibration. Applied Energy, 286:116492, 2021.
Adrian Chong and Kathrin Menberg. Guidelines for the bayesian calibration of building energy models. Energy and Buildings, 174:527–547, 2018.
Nicolas Chopin, Pierre E Jacob, and Omiros Papaspiliopoulos. Smc2: an efficient algorithm for sequential analysis of state space models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 75(3):397–426, 2013.
Arnaud Doucet, Nando de Freitas, Kevin Murphy, and Stuart Russell. Rao-blackwellised particle filtering for dynamic bayesian networks. In Uncertainty in Artificial Intelligence (UAI), 176–183. San Francisco, CA, 2000.
Jonah Gabry, Daniel Simpson, Aki Vehtari, Michael Betancourt, and Andrew Gelman. Visualization in bayesian workflow. Journal of the Royal Statistical Society Series A: Statistics in Society, 182(2):389–402, 2019.
Andrew Gelman, John B Carlin, Hal S Stern, David B Dunson, Aki Vehtari, and Donald B Rubin. Bayesian data analysis. CRC press, 2013.
Tilmann Gneiting and Adrian E Raftery. Strictly proper scoring rules, prediction, and estimation. Journal of the American statistical Association, 102(477):359–378, 2007.
Trevor Hastie, Robert Tibshirani, and Jerome Friedman. The elements of statistical learning: data mining, inference, and prediction. Springer Science & Business Media, 2009.
Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani. An introduction to statistical learning. Volume 112. Springer, 2013.
JCGM. Evaluation of measurement data—guide to the expression of uncertainty in measurement. Int. Organ. Stand. Geneva ISBN, 50:134, 2008.
Nikolas Kantas, Arnaud Doucet, Sumeetpal S Singh, Jan Maciejowski, Nicolas Chopin, and others. On particle methods for parameter estimation in state-space models. Statistical science, 30(3):328–351, 2015.
Lukas Lundström and Jan Akander. Bayesian calibration with augmented stochastic state-space models of district-heated multifamily buildings. Energies, 13(1):76, 2019.
Henrik Madsen and Jan Holst. Estimation of continuous-time models for the heat dynamics of a building. Energy and buildings, 22(1):67–79, 1995.
Denis Maillet. Problèmes inverses en diffusion thermique. Ed. Techniques Ingénieur, 2010.
Richard McElreath. Statistical rethinking: A Bayesian course with examples in R and Stan. Chapman and Hall/CRC, 2018.
Kevin Patrick Murphy. Dynamic bayesian networks: representation, inference and learning. University of California, Berkeley Berkeley, CA, 2002.
T Agami Reddy. Literature review on calibration of building energy simulation programs: uses, problems, procedures, uncertainty, and tools. ASHRAE transactions, 112:226, 2006.
Simon Rouchier. Solving inverse problems in building physics: an overview of guidelines for a careful and optimal use of data. Energy and Buildings, 166:178–195, 2018.
Simon Rouchier. Bayesian workflow and hidden markov energy-signature model for measurement and verification. Energies, 15(10):3534, 2022.
Simon Rouchier, Maria José Jiménez, and Sergio Castaño. Sequential monte carlo for on-line parameter estimation of a lumped building energy model. Energy and Buildings, 187:86–94, 2019.
Simon Rouchier, Mickaël Rabouille, and Pierre Oberlé. Calibration of simplified building energy models for parameter estimation and forecasting: stochastic versus deterministic modelling. Building and Environment, 134:181–190, 2018.
Robert H Shumway, David S Stoffer, and David S Stoffer. Time series analysis and its applications. Volume 3. Springer, 2000.
Simo Särkkä. Bayesian filtering and smoothing. Cambridge University Press, 2013.
Aki Vehtari, Andrew Gelman, and Jonah Bagry. Practical bayesian model evaluation using leave-one-out cross-validation and waic. Statistics and Computing, 27:1413–1432, Aug 2016.
Aki Vehtari, Andrew Gelman, Daniel Simpson, Bob Carpenter, and Paul-Christian Bürkner. Rank-normalization, folding, and localization: an improved r for assessing convergence of mcmc (with discussion). Bayesian analysis, 16(2):667–718, 2021.
Sumio Watanabe and Manfred Opper. Asymptotic equivalence of bayes cross validation and widely applicable information criterion in singular learning theory. Journal of machine learning research, 2010.
Sebastian Wolf, Jan Kloppenborg Møller, Magnus Alexander Bitsch, John Krogstie, and Henrik Madsen. A markov-switching model for building occupant activity estimation. Energy and Buildings, 183:672–683, 2019.
Onno Zoeter and T Heskes. Expectation propagation and generalised ep methods for inference in switching linear dynamical systems. Cambridge University Press, 2011.