{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:OHLMLONIBMWFMQMDQ73EWU3YIS","short_pith_number":"pith:OHLMLONI","schema_version":"1.0","canonical_sha256":"71d6c5b9a80b2c56418387f64b5378448cf90008b8fa750be536b31002d5941b","source":{"kind":"arxiv","id":"2309.09073","version":1},"attestation_state":"computed","paper":{"title":"Enhancing personalised thermal comfort models with Active Learning for improved HVAC controls","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Adrian Chong, Xilei Dai, Yue Lei, Zeynep Duygu Tekler","submitted_at":"2023-09-16T18:42:58Z","abstract_excerpt":"Developing personalised thermal comfort models to inform occupant-centric controls (OCC) in buildings requires collecting large amounts of real-time occupant preference data. This process can be highly intrusive and labour-intensive for large-scale implementations, limiting the practicality of real-world OCC implementations. To address this issue, this study proposes a thermal preference-based HVAC control framework enhanced with Active Learning (AL) to address the data challenges related to real-world implementations of such OCC systems. The proposed AL approach proactively identifies the mos"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2309.09073","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-09-16T18:42:58Z","cross_cats_sorted":[],"title_canon_sha256":"2484fca766b73684e9ecbbb417e89a2931e64d645e0b1283ae8a4ba677151bd0","abstract_canon_sha256":"c8a884e92201ce1223e8c901b184f88452f2c27f91ce248aea01af5f7620b6f0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:51:34.233072Z","signature_b64":"SYZEjduLXc9XMaT0vwRV/Yh7WtxKE5Ebo/Yz5yn1jNAp98rHTZqJBOmvQx0V9fBVN5tFJioanYfQaNk3wyc7AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"71d6c5b9a80b2c56418387f64b5378448cf90008b8fa750be536b31002d5941b","last_reissued_at":"2026-07-05T06:51:34.232674Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:51:34.232674Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Enhancing personalised thermal comfort models with Active Learning for improved HVAC controls","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Adrian Chong, Xilei Dai, Yue Lei, Zeynep Duygu Tekler","submitted_at":"2023-09-16T18:42:58Z","abstract_excerpt":"Developing personalised thermal comfort models to inform occupant-centric controls (OCC) in buildings requires collecting large amounts of real-time occupant preference data. This process can be highly intrusive and labour-intensive for large-scale implementations, limiting the practicality of real-world OCC implementations. To address this issue, this study proposes a thermal preference-based HVAC control framework enhanced with Active Learning (AL) to address the data challenges related to real-world implementations of such OCC systems. The proposed AL approach proactively identifies the mos"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.09073","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2309.09073/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2309.09073","created_at":"2026-07-05T06:51:34.232729+00:00"},{"alias_kind":"arxiv_version","alias_value":"2309.09073v1","created_at":"2026-07-05T06:51:34.232729+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.09073","created_at":"2026-07-05T06:51:34.232729+00:00"},{"alias_kind":"pith_short_12","alias_value":"OHLMLONIBMWF","created_at":"2026-07-05T06:51:34.232729+00:00"},{"alias_kind":"pith_short_16","alias_value":"OHLMLONIBMWFMQMD","created_at":"2026-07-05T06:51:34.232729+00:00"},{"alias_kind":"pith_short_8","alias_value":"OHLMLONI","created_at":"2026-07-05T06:51:34.232729+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OHLMLONIBMWFMQMDQ73EWU3YIS","json":"https://pith.science/pith/OHLMLONIBMWFMQMDQ73EWU3YIS.json","graph_json":"https://pith.science/api/pith-number/OHLMLONIBMWFMQMDQ73EWU3YIS/graph.json","events_json":"https://pith.science/api/pith-number/OHLMLONIBMWFMQMDQ73EWU3YIS/events.json","paper":"https://pith.science/paper/OHLMLONI"},"agent_actions":{"view_html":"https://pith.science/pith/OHLMLONIBMWFMQMDQ73EWU3YIS","download_json":"https://pith.science/pith/OHLMLONIBMWFMQMDQ73EWU3YIS.json","view_paper":"https://pith.science/paper/OHLMLONI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2309.09073&json=true","fetch_graph":"https://pith.science/api/pith-number/OHLMLONIBMWFMQMDQ73EWU3YIS/graph.json","fetch_events":"https://pith.science/api/pith-number/OHLMLONIBMWFMQMDQ73EWU3YIS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OHLMLONIBMWFMQMDQ73EWU3YIS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OHLMLONIBMWFMQMDQ73EWU3YIS/action/storage_attestation","attest_author":"https://pith.science/pith/OHLMLONIBMWFMQMDQ73EWU3YIS/action/author_attestation","sign_citation":"https://pith.science/pith/OHLMLONIBMWFMQMDQ73EWU3YIS/action/citation_signature","submit_replication":"https://pith.science/pith/OHLMLONIBMWFMQMDQ73EWU3YIS/action/replication_record"}},"created_at":"2026-07-05T06:51:34.232729+00:00","updated_at":"2026-07-05T06:51:34.232729+00:00"}