{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:RQIXSBFAHNBYB4ZUE3LQURSPGS","short_pith_number":"pith:RQIXSBFA","schema_version":"1.0","canonical_sha256":"8c117904a03b4380f33426d70a464f3481bd1b304dcf2ea486659380f1468b23","source":{"kind":"arxiv","id":"1702.03613","version":1},"attestation_state":"computed","paper":{"title":"A Multi-model Combination Approach for Probabilistic Wind Power Forecasting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP"],"primary_cat":"cs.LG","authors_text":"Can Wan, Jianhui Wang, Ming Yang, Yonghua Song, You Lin","submitted_at":"2017-02-13T02:48:16Z","abstract_excerpt":"Short-term probabilistic wind power forecasting can provide critical quantified uncertainty information of wind generation for power system operation and control. As the complicated characteristics of wind power prediction error, it would be difficult to develop a universal forecasting model dominating over other alternative models. Therefore, a novel multi-model combination (MMC) approach for short-term probabilistic wind generation forecasting is proposed in this paper to exploit the advantages of different forecasting models. The proposed approach can combine different forecasting models th"},"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":"1702.03613","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-02-13T02:48:16Z","cross_cats_sorted":["stat.AP"],"title_canon_sha256":"fcc6dcaa50bbeec24e8e26b92523a1ed539306bbcad0da61301d0ae93fbed067","abstract_canon_sha256":"ddc05639d6798dcaec51e18e0cc5c2a4da08fd83abd15ee3432baa253a47ca06"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:50:53.396803Z","signature_b64":"HW0ZptCTTho6Whzcdz6RJXm6CqgyjTurxNG5PkUFtyLD55AILQ7qPE5tGCJCSV/GGdxQkspz//3UROGFd6rvCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8c117904a03b4380f33426d70a464f3481bd1b304dcf2ea486659380f1468b23","last_reissued_at":"2026-05-18T00:50:53.396332Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:50:53.396332Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Multi-model Combination Approach for Probabilistic Wind Power Forecasting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP"],"primary_cat":"cs.LG","authors_text":"Can Wan, Jianhui Wang, Ming Yang, Yonghua Song, You Lin","submitted_at":"2017-02-13T02:48:16Z","abstract_excerpt":"Short-term probabilistic wind power forecasting can provide critical quantified uncertainty information of wind generation for power system operation and control. As the complicated characteristics of wind power prediction error, it would be difficult to develop a universal forecasting model dominating over other alternative models. Therefore, a novel multi-model combination (MMC) approach for short-term probabilistic wind generation forecasting is proposed in this paper to exploit the advantages of different forecasting models. The proposed approach can combine different forecasting models th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1702.03613","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":""},"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":"1702.03613","created_at":"2026-05-18T00:50:53.396402+00:00"},{"alias_kind":"arxiv_version","alias_value":"1702.03613v1","created_at":"2026-05-18T00:50:53.396402+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1702.03613","created_at":"2026-05-18T00:50:53.396402+00:00"},{"alias_kind":"pith_short_12","alias_value":"RQIXSBFAHNBY","created_at":"2026-05-18T12:31:39.905425+00:00"},{"alias_kind":"pith_short_16","alias_value":"RQIXSBFAHNBYB4ZU","created_at":"2026-05-18T12:31:39.905425+00:00"},{"alias_kind":"pith_short_8","alias_value":"RQIXSBFA","created_at":"2026-05-18T12:31:39.905425+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/RQIXSBFAHNBYB4ZUE3LQURSPGS","json":"https://pith.science/pith/RQIXSBFAHNBYB4ZUE3LQURSPGS.json","graph_json":"https://pith.science/api/pith-number/RQIXSBFAHNBYB4ZUE3LQURSPGS/graph.json","events_json":"https://pith.science/api/pith-number/RQIXSBFAHNBYB4ZUE3LQURSPGS/events.json","paper":"https://pith.science/paper/RQIXSBFA"},"agent_actions":{"view_html":"https://pith.science/pith/RQIXSBFAHNBYB4ZUE3LQURSPGS","download_json":"https://pith.science/pith/RQIXSBFAHNBYB4ZUE3LQURSPGS.json","view_paper":"https://pith.science/paper/RQIXSBFA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1702.03613&json=true","fetch_graph":"https://pith.science/api/pith-number/RQIXSBFAHNBYB4ZUE3LQURSPGS/graph.json","fetch_events":"https://pith.science/api/pith-number/RQIXSBFAHNBYB4ZUE3LQURSPGS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RQIXSBFAHNBYB4ZUE3LQURSPGS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RQIXSBFAHNBYB4ZUE3LQURSPGS/action/storage_attestation","attest_author":"https://pith.science/pith/RQIXSBFAHNBYB4ZUE3LQURSPGS/action/author_attestation","sign_citation":"https://pith.science/pith/RQIXSBFAHNBYB4ZUE3LQURSPGS/action/citation_signature","submit_replication":"https://pith.science/pith/RQIXSBFAHNBYB4ZUE3LQURSPGS/action/replication_record"}},"created_at":"2026-05-18T00:50:53.396402+00:00","updated_at":"2026-05-18T00:50:53.396402+00:00"}