{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:FWEIOLLKWMCG75DSQR6P6KYSJB","short_pith_number":"pith:FWEIOLLK","schema_version":"1.0","canonical_sha256":"2d88872d6ab3046ff472847cff2b124853fb3c718a323bdc2e1d033b27f96663","source":{"kind":"arxiv","id":"2501.10827","version":1},"attestation_state":"computed","paper":{"title":"Integrating Expert and Physics Knowledge for Modeling Heat Load in District Heating Systems","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Francisco Souza, Geert Postma, Jeroen Jansen, Thom Badings","submitted_at":"2025-01-18T17:19:09Z","abstract_excerpt":"New residential neighborhoods are often supplied with heat via district heating systems (DHS). Improving the energy efficiency of a DHS is critical for increasing sustainability and satisfying user requirements. In this paper, we present HELIOS, a dedicated artificial intelligence (AI) model designed specifically for modeling the heat load in DHS. HELIOS leverages a combination of established physical principles and expert knowledge, resulting in superior performance compared to existing state-of-the-art models. HELIOS is explainable, enabling enhanced accountability and traceability in its pr"},"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":"2501.10827","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"eess.SY","submitted_at":"2025-01-18T17:19:09Z","cross_cats_sorted":["cs.SY"],"title_canon_sha256":"7403bf98eab81ca0068cf66990cffcdfbfa91c36e7a31c5571c38dfc5bf2e279","abstract_canon_sha256":"e6c2eaa5cbf2fbd7bfab09e04d4076a52dc26b43d3e401574947c6a6b51f1179"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:02:33.841282Z","signature_b64":"o/EnuoJpiHJKD42sJ3hDbucpJFcCpVEMD/W/J8nYBlSto42zK4IJSQYZRoqluUNMh5gYgB6vdRGP9+wsCsE0Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2d88872d6ab3046ff472847cff2b124853fb3c718a323bdc2e1d033b27f96663","last_reissued_at":"2026-07-05T10:02:33.840915Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:02:33.840915Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Integrating Expert and Physics Knowledge for Modeling Heat Load in District Heating Systems","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Francisco Souza, Geert Postma, Jeroen Jansen, Thom Badings","submitted_at":"2025-01-18T17:19:09Z","abstract_excerpt":"New residential neighborhoods are often supplied with heat via district heating systems (DHS). Improving the energy efficiency of a DHS is critical for increasing sustainability and satisfying user requirements. In this paper, we present HELIOS, a dedicated artificial intelligence (AI) model designed specifically for modeling the heat load in DHS. HELIOS leverages a combination of established physical principles and expert knowledge, resulting in superior performance compared to existing state-of-the-art models. HELIOS is explainable, enabling enhanced accountability and traceability in its pr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.10827","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/2501.10827/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":"2501.10827","created_at":"2026-07-05T10:02:33.840968+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.10827v1","created_at":"2026-07-05T10:02:33.840968+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.10827","created_at":"2026-07-05T10:02:33.840968+00:00"},{"alias_kind":"pith_short_12","alias_value":"FWEIOLLKWMCG","created_at":"2026-07-05T10:02:33.840968+00:00"},{"alias_kind":"pith_short_16","alias_value":"FWEIOLLKWMCG75DS","created_at":"2026-07-05T10:02:33.840968+00:00"},{"alias_kind":"pith_short_8","alias_value":"FWEIOLLK","created_at":"2026-07-05T10:02:33.840968+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/FWEIOLLKWMCG75DSQR6P6KYSJB","json":"https://pith.science/pith/FWEIOLLKWMCG75DSQR6P6KYSJB.json","graph_json":"https://pith.science/api/pith-number/FWEIOLLKWMCG75DSQR6P6KYSJB/graph.json","events_json":"https://pith.science/api/pith-number/FWEIOLLKWMCG75DSQR6P6KYSJB/events.json","paper":"https://pith.science/paper/FWEIOLLK"},"agent_actions":{"view_html":"https://pith.science/pith/FWEIOLLKWMCG75DSQR6P6KYSJB","download_json":"https://pith.science/pith/FWEIOLLKWMCG75DSQR6P6KYSJB.json","view_paper":"https://pith.science/paper/FWEIOLLK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.10827&json=true","fetch_graph":"https://pith.science/api/pith-number/FWEIOLLKWMCG75DSQR6P6KYSJB/graph.json","fetch_events":"https://pith.science/api/pith-number/FWEIOLLKWMCG75DSQR6P6KYSJB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FWEIOLLKWMCG75DSQR6P6KYSJB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FWEIOLLKWMCG75DSQR6P6KYSJB/action/storage_attestation","attest_author":"https://pith.science/pith/FWEIOLLKWMCG75DSQR6P6KYSJB/action/author_attestation","sign_citation":"https://pith.science/pith/FWEIOLLKWMCG75DSQR6P6KYSJB/action/citation_signature","submit_replication":"https://pith.science/pith/FWEIOLLKWMCG75DSQR6P6KYSJB/action/replication_record"}},"created_at":"2026-07-05T10:02:33.840968+00:00","updated_at":"2026-07-05T10:02:33.840968+00:00"}