{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:WMPITZEBRQQQSIYMCJRVKQH67B","short_pith_number":"pith:WMPITZEB","schema_version":"1.0","canonical_sha256":"b31e89e4818c2109230c12635540fef8789a29fe070b0fbbe8921a04be538e1d","source":{"kind":"arxiv","id":"2510.15780","version":2},"attestation_state":"computed","paper":{"title":"Enhanced Renewable Energy Forecasting using Context-Aware Conformal Prediction","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.AP","authors_text":"Alireza Moradi, Mathieu Tanneau, Pascal Van Hentenryck, Reza Zandehshahvar","submitted_at":"2025-10-17T16:02:46Z","abstract_excerpt":"Artificial intelligence (AI) is increasingly used to support renewable energy forecasting and grid operations. As renewable penetration grows, reliable probabilistic forecasting is becoming essential for managing uncertainty and supporting risk-aware operational decision-making. However, these forecasts often suffer from miscalibration due to temporal variability, changing weather conditions, and heterogeneous operating regimes. In many real-world settings, renewable energy forecasts are provided by external sources, vendors, or independently trained systems, making retraining infeasible becau"},"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":"2510.15780","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"stat.AP","submitted_at":"2025-10-17T16:02:46Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"3ca1180d9d3829de731dad5a8f291600a875c49a4bcab07f94674e8e2111949a","abstract_canon_sha256":"220b50e8b87de101b44c36ddcfdb2c7d8deaf61a75e82431f4a9d712e1224fb9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-03T01:05:06.519478Z","signature_b64":"rBpnwtjS84uSad0dhMUd+BrvyC0v2VQjxdp9wfzxpRjAVrXFQYtQy5L7NiDJgW5pYyPTJ4oTtoIGg+j4CAgnDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b31e89e4818c2109230c12635540fef8789a29fe070b0fbbe8921a04be538e1d","last_reissued_at":"2026-06-03T01:05:06.518806Z","signature_status":"signed_v1","first_computed_at":"2026-06-03T01:05:06.518806Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Enhanced Renewable Energy Forecasting using Context-Aware Conformal Prediction","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.AP","authors_text":"Alireza Moradi, Mathieu Tanneau, Pascal Van Hentenryck, Reza Zandehshahvar","submitted_at":"2025-10-17T16:02:46Z","abstract_excerpt":"Artificial intelligence (AI) is increasingly used to support renewable energy forecasting and grid operations. As renewable penetration grows, reliable probabilistic forecasting is becoming essential for managing uncertainty and supporting risk-aware operational decision-making. However, these forecasts often suffer from miscalibration due to temporal variability, changing weather conditions, and heterogeneous operating regimes. In many real-world settings, renewable energy forecasts are provided by external sources, vendors, or independently trained systems, making retraining infeasible becau"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2510.15780","kind":"arxiv","version":2},"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/2510.15780/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":"2510.15780","created_at":"2026-06-03T01:05:06.518988+00:00"},{"alias_kind":"arxiv_version","alias_value":"2510.15780v2","created_at":"2026-06-03T01:05:06.518988+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2510.15780","created_at":"2026-06-03T01:05:06.518988+00:00"},{"alias_kind":"pith_short_12","alias_value":"WMPITZEBRQQQ","created_at":"2026-06-03T01:05:06.518988+00:00"},{"alias_kind":"pith_short_16","alias_value":"WMPITZEBRQQQSIYM","created_at":"2026-06-03T01:05:06.518988+00:00"},{"alias_kind":"pith_short_8","alias_value":"WMPITZEB","created_at":"2026-06-03T01:05:06.518988+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/WMPITZEBRQQQSIYMCJRVKQH67B","json":"https://pith.science/pith/WMPITZEBRQQQSIYMCJRVKQH67B.json","graph_json":"https://pith.science/api/pith-number/WMPITZEBRQQQSIYMCJRVKQH67B/graph.json","events_json":"https://pith.science/api/pith-number/WMPITZEBRQQQSIYMCJRVKQH67B/events.json","paper":"https://pith.science/paper/WMPITZEB"},"agent_actions":{"view_html":"https://pith.science/pith/WMPITZEBRQQQSIYMCJRVKQH67B","download_json":"https://pith.science/pith/WMPITZEBRQQQSIYMCJRVKQH67B.json","view_paper":"https://pith.science/paper/WMPITZEB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2510.15780&json=true","fetch_graph":"https://pith.science/api/pith-number/WMPITZEBRQQQSIYMCJRVKQH67B/graph.json","fetch_events":"https://pith.science/api/pith-number/WMPITZEBRQQQSIYMCJRVKQH67B/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WMPITZEBRQQQSIYMCJRVKQH67B/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WMPITZEBRQQQSIYMCJRVKQH67B/action/storage_attestation","attest_author":"https://pith.science/pith/WMPITZEBRQQQSIYMCJRVKQH67B/action/author_attestation","sign_citation":"https://pith.science/pith/WMPITZEBRQQQSIYMCJRVKQH67B/action/citation_signature","submit_replication":"https://pith.science/pith/WMPITZEBRQQQSIYMCJRVKQH67B/action/replication_record"}},"created_at":"2026-06-03T01:05:06.518988+00:00","updated_at":"2026-06-03T01:05:06.518988+00:00"}