{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/estimating-buildings-parameters-over-time","title":"Estimating Buildings' Parameters over Time Including Prior Knowledge","arxiv_id":"1901.07469","date":"2019-01-09","proceeding":null,"authors":["Nilavra Pathak","James Foulds","Nirmalya Roy","Nilanjan Banerjee","Ryan Robucci"],"abstract":"Modeling buildings' heat dynamics is a complex process which depends on\nvarious factors including weather, building thermal capacity, insulation\npreservation, and residents' behavior. Gray-box models offer a causal inference\nof those dynamics expressed in few parameters specific to built environments.\nThese parameters can provide compelling insights into the characteristics of\nbuilding artifacts and have various applications such as forecasting HVAC\nusage, indoor temperature control monitoring of built environments, etc. In\nthis paper, we present a systematic study of modeling buildings' thermal\ncharacteristics and thus derive the parameters of built conditions with a\nBayesian approach. We build a Bayesian state-space model that can adapt and\nincorporate buildings' thermal equations and propose a generalized solution\nthat can easily adapt prior knowledge regarding the parameters. We show that a\nfaster approximate approach using variational inference for parameter\nestimation can provide similar parameters as that of a more time-consuming\nMarkov Chain Monte Carlo (MCMC) approach. We perform extensive evaluations on\ntwo datasets to understand the generative process and show that the Bayesian\napproach is more interpretable. We further study the effects of prior selection\nfor the model parameters and transfer learning, where we learn parameters from\none season and use them to fit the model in the other. We perform extensive\nevaluations on controlled and real data traces to enumerate buildings'\nparameter within a 95% credible interval.","url_abs":"http://arxiv.org/abs/1901.07469v3","url_pdf":"http://arxiv.org/pdf/1901.07469v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"estimating-buildings-parameters-over-time","repo_url":"https://github.com/Nilavro/BSSPy","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"causal-inference","task_name":"Causal Inference"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"},{"task_slug":"variational-inference","task_name":"Variational Inference"},{"task_slug":"parameter-estimation","task_name":"parameter estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}