{"doi":"10.1145/3600100.3626280","title":"Thermal discomfort prediction with sparse residential thermostat dataset","abstract":null,"journal":"Proceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation","year":2023,"id":648187,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":0.0,"corpus_rank":10616,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1689071,"name":"Michael Zeifman","orcid":"0000-0003-2018-257X","position":1,"is_corresponding":false},{"id":1689072,"name":"Kurt Roth","orcid":"0009-0009-7313-8530","position":2,"is_corresponding":false},{"id":1689070,"name":"Hannah Fontenot","orcid":"0000-0002-0098-9803","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Thermal discomfort prediction with sparse residential thermostat dataset","abstract":"We develop a probabilistic method for predicting the thermal comfort of residential occupants during demand response (DR) events. Specifically, we estimate the probability that occupants will change the thermostat setpoint, by calculating their discomfort tolerance based on the degree and duration of discomfort. We also show that we can predict this discomfort tolerance reliably.","is_dataset_classified":null,"base_score":0.0,"endowment":0.0,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19965766","pmcid":null,"openalex_id":"https://openalex.org/W4388320601","authors":[],"funders":[{"funder_name":"DOE U.S. Department of Energy","grant_id":"DE-EE 0009696","title":null}],"total_grants":1,"fwci":0.0,"citation_percentile":0.28620904,"influential_citations":0,"citation_trend":[],"oa_status":"gold","license":"https://www.acm.org/publications/policies/copyright_policy#Background","oa_locations":[{"url":"https://dl.acm.org/doi/pdf/10.1145/3600100.3626280","host_type":""},{"url":"https://dl.acm.org/doi/pdf/10.1145/3600100.3626280","host_type":""},{"url":"https://dl.acm.org/doi/10.1145/3600100.3626280","host_type":"publisher"},{"url":"https://doi.org/10.1145/3600100.3626280","host_type":""}],"fields_of_study":["Building Energy and Comfort Optimization","Urban Heat Island Mitigation","Image and Video Quality Assessment"],"mesh_terms":[],"keywords":["Setpoint","Thermostat","Thermal comfort","Probabilistic logic","Computer science","Duration (music)","Degree (music)","Statistics","Simulation","Environmental science","Mathematics","Artificial intelligence","Engineering","Meteorology","Mechanical engineering"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Sustainable cities and communities"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-10T02:20:26.156354Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}