{"id":{"repo_id":"cornell","oai_identifier":"oai:ecommons.cornell.edu:1813/44364"},"canonical_url":"https://search.dev.ndltd.org/etd/cornell/oai:ecommons.cornell.edu:1813/44364","repository":{"repo_id":"cornell","name":"Cornell University","base_url":"https://ecommons.cornell.edu/server/oai/request"},"display":{"title":"Applied Probabilistic Inference: Model Estimation For Hvac Predictive Controls And All-Weather Perception For Autonomous Vehicles","abstract":"Probabilistic inference and reasoning is applied to two major application areas: HVAC controls in buildings and autonomous vehicle perception. Although the physical domains differ vastly, across both applications the presented novel contributions share real-time inference of stochastic systems for improved control capability and performance. Besides performing simple state estimation, Kalman Filters in both applications are extended for model inference-estimating thermal model parameters and disturbances in buildings and dynamic object classification for perception in autonomous vehicles. Part one of this study proposes a general, scalable method to learn controloriented thermal models of buildings that could enable wide-scale deployment of cost-effective predictive controls. An Unscented Kalman Filter augmented for parameter and disturbance estimation is shown to accurately learn and predict a building's thermal response. By leveraging building topology and measurement data, the filter quickly learns parameters of a thermal network during periods of known or constrained loads and then characterizes unknown loads in order to provide accurate 24+ hour energy predictions. Performance was validated with EnergyPlus simulation data across a year-long study of a passive building. The method is extended to multi-zone actively controlled buildings by using the controller to excite unknown portions of the building's dynamics. A simulation study demonstrates self-excitation improves model estimation. Formalization of parameterization, disturbance estimation, and self-excitation routines is shown with an observability analysis. Comparing against a baseline thermostat controller, a Model Predictive Control (MPC) framework, which anticipates weather uncertainty and time-varying temperature set-points, is shown to improve energy savings and occupant comfort. Part two of this study presents a novel probabilistic perception algorithm as a real-time joint solution to data association, object tracking, and object classification for an autonomous ground vehicle (AGV) in all-weather conditions. The presented algorithm extends a Rao-Blackwellized Particle Filter originally built for Cornell's AGV for the DARPA Urban Challenge (DUC) to include multiple model tracking for classification. Additionally a state-of-the-art vision detection algorithm that includes heading information for AGV applications was implemented. Cornell's AGV from the DUC was upgraded and used to experimentally examine if and how state-of-the-art vision algorithms can complement or replace lidar and radar sensors. Sensor and algorithm performance in adverse weather and lighting conditions is tested. Experimental evaluation demonstrates that sensor diversity with a joint probabilistic perception algorithm provides robust all-weather data association, tracking, and classification.","abstract_html":"Probabilistic inference and reasoning is applied to two major application areas: HVAC controls in buildings and autonomous vehicle perception. Although the physical domains differ vastly, across both applications the presented novel contributions share real-time inference of stochastic systems for improved control capability and performance. Besides performing simple state estimation, Kalman Filters in both applications are extended for model inference-estimating thermal model parameters and disturbances in buildings and dynamic object classification for perception in autonomous vehicles. Part one of this study proposes a general, scalable method to learn controloriented thermal models of buildings that could enable wide-scale deployment of cost-effective predictive controls. An Unscented Kalman Filter augmented for parameter and disturbance estimation is shown to accurately learn and predict a building's thermal response. By leveraging building topology and measurement data, the filter quickly learns parameters of a thermal network during periods of known or constrained loads and then characterizes unknown loads in order to provide accurate 24+ hour energy predictions. Performance was validated with EnergyPlus simulation data across a year-long study of a passive building. The method is extended to multi-zone actively controlled buildings by using the controller to excite unknown portions of the building's dynamics. A simulation study demonstrates self-excitation improves model estimation. Formalization of parameterization, disturbance estimation, and self-excitation routines is shown with an observability analysis. Comparing against a baseline thermostat controller, a Model Predictive Control (MPC) framework, which anticipates weather uncertainty and time-varying temperature set-points, is shown to improve energy savings and occupant comfort. Part two of this study presents a novel probabilistic perception algorithm as a real-time joint solution to data association, object tracking, and object classification for an autonomous ground vehicle (AGV) in all-weather conditions. The presented algorithm extends a Rao-Blackwellized Particle Filter originally built for Cornell's AGV for the DARPA Urban Challenge (DUC) to include multiple model tracking for classification. Additionally a state-of-the-art vision detection algorithm that includes heading information for AGV applications was implemented. Cornell's AGV from the DUC was upgraded and used to experimentally examine if and how state-of-the-art vision algorithms can complement or replace lidar and radar sensors. Sensor and algorithm performance in adverse weather and lighting conditions is tested. Experimental evaluation demonstrates that sensor diversity with a joint probabilistic perception algorithm provides robust all-weather data association, tracking, and classification.","abstract_has_math":false,"creators":["Radecki, Peter"],"institution":"Cornell University","degree_name":"Ph. D., Mechanical Engineering","degree_level":"Doctor of Philosophy","degree_discipline":"Mechanical Engineering","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":["Snavely,Keith Noah","Hencey,Brandon M."],"year":2016,"date_issued":"2016-05-29","date_published":"2016-05-29","updated_at":"2026-07-24T01:49:10Z","subjects":["Kalman Filter","Probabilistic Inference","Model Predictive Control"],"languages":["en_US"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.7298/X4MK69TG"],"render_values":[{"text":"https://doi.org/10.7298/X4MK69TG","href":"https://doi.org/10.7298/X4MK69TG","code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/1813/44364","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Snavely,Keith Noah","Hencey,Brandon M."]},{"key":"dc:creator","label":"Author","values":["Radecki, Peter"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2016-07-05T15:30:13Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2016-07-05T15:30:13Z"]},{"key":"dc:date.issued","label":"Date","values":["2016-05-29"]},{"key":"dc:type","label":"Dc Type","values":["dissertation or thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Doctor of Philosophy"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph. 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By leveraging building topology and measurement data, the filter quickly learns parameters of a thermal network during periods of known or constrained loads and then characterizes unknown loads in order to provide accurate 24+ hour energy predictions. Performance was validated with EnergyPlus simulation data across a year-long study of a passive building. The method is extended to multi-zone actively controlled buildings by using the controller to excite unknown portions of the building's dynamics. A simulation study demonstrates self-excitation improves model estimation. Formalization of parameterization, disturbance estimation, and self-excitation routines is shown with an observability analysis. Comparing against a baseline thermostat controller, a Model Predictive Control (MPC) framework, which anticipates weather uncertainty and time-varying temperature set-points, is shown to improve energy savings and occupant comfort. Part two of this study presents a novel probabilistic perception algorithm as a real-time joint solution to data association, object tracking, and object classification for an autonomous ground vehicle (AGV) in all-weather conditions. The presented algorithm extends a Rao-Blackwellized Particle Filter originally built for Cornell's AGV for the DARPA Urban Challenge (DUC) to include multiple model tracking for classification. Additionally a state-of-the-art vision detection algorithm that includes heading information for AGV applications was implemented. Cornell's AGV from the DUC was upgraded and used to experimentally examine if and how state-of-the-art vision algorithms can complement or replace lidar and radar sensors. Sensor and algorithm performance in adverse weather and lighting conditions is tested. Experimental evaluation demonstrates that sensor diversity with a joint probabilistic perception algorithm provides robust all-weather data association, tracking, and classification."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Applied Probabilistic Inference: Model Estimation For Hvac Predictive Controls And All-Weather Perception For Autonomous Vehicles"]}]}],"canonical_facts":{"dc:contributor.committeemember":["Snavely,Keith Noah","Hencey,Brandon M."],"dc:creator":["Radecki, Peter"],"dc:date.accessioned":["2016-07-05T15:30:13Z"],"dc:date.available":["2016-07-05T15:30:13Z"],"dc:date.issued":["2016-05-29"],"dc:description.abstract":["Probabilistic inference and reasoning is applied to two major application areas: HVAC controls in buildings and autonomous vehicle perception. Although the physical domains differ vastly, across both applications the presented novel contributions share real-time inference of stochastic systems for improved control capability and performance. Besides performing simple state estimation, Kalman Filters in both applications are extended for model inference-estimating thermal model parameters and disturbances in buildings and dynamic object classification for perception in autonomous vehicles. Part one of this study proposes a general, scalable method to learn controloriented thermal models of buildings that could enable wide-scale deployment of cost-effective predictive controls. An Unscented Kalman Filter augmented for parameter and disturbance estimation is shown to accurately learn and predict a building's thermal response. By leveraging building topology and measurement data, the filter quickly learns parameters of a thermal network during periods of known or constrained loads and then characterizes unknown loads in order to provide accurate 24+ hour energy predictions. Performance was validated with EnergyPlus simulation data across a year-long study of a passive building. The method is extended to multi-zone actively controlled buildings by using the controller to excite unknown portions of the building's dynamics. A simulation study demonstrates self-excitation improves model estimation. Formalization of parameterization, disturbance estimation, and self-excitation routines is shown with an observability analysis. Comparing against a baseline thermostat controller, a Model Predictive Control (MPC) framework, which anticipates weather uncertainty and time-varying temperature set-points, is shown to improve energy savings and occupant comfort. Part two of this study presents a novel probabilistic perception algorithm as a real-time joint solution to data association, object tracking, and object classification for an autonomous ground vehicle (AGV) in all-weather conditions. The presented algorithm extends a Rao-Blackwellized Particle Filter originally built for Cornell's AGV for the DARPA Urban Challenge (DUC) to include multiple model tracking for classification. Additionally a state-of-the-art vision detection algorithm that includes heading information for AGV applications was implemented. Cornell's AGV from the DUC was upgraded and used to experimentally examine if and how state-of-the-art vision algorithms can complement or replace lidar and radar sensors. Sensor and algorithm performance in adverse weather and lighting conditions is tested. Experimental evaluation demonstrates that sensor diversity with a joint probabilistic perception algorithm provides robust all-weather data association, tracking, and classification."],"dc:format.mimetype":["application/pdf"],"dc:identifier.doi":["https://doi.org/10.7298/X4MK69TG"],"dc:identifier.uri":["https://hdl.handle.net/1813/44364"],"dc:language.iso":["en_US"],"dc:subject":["Kalman Filter","Probabilistic Inference","Model Predictive Control"],"dc:title":["Applied Probabilistic Inference: Model Estimation For Hvac Predictive Controls And All-Weather Perception For Autonomous Vehicles"],"dc:type":["dissertation or thesis"],"thesis:degree_discipline":["Mechanical Engineering"],"thesis:degree_level":["Doctor of Philosophy"],"thesis:degree_name":["Ph. D., Mechanical Engineering"],"thesis:institution_name":["Cornell University"]},"updated_at":"2026-07-24T01:49:10Z"}