{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:7WZXXY6TS74ICFSHSFK4FGGXUO","short_pith_number":"pith:7WZXXY6T","schema_version":"1.0","canonical_sha256":"fdb37be3d397f88116479155c298d7a395cf7f073ac4022ac8044b1a4058a3f8","source":{"kind":"arxiv","id":"1903.05274","version":1},"attestation_state":"computed","paper":{"title":"Forecasting Spatio-Temporal Renewable Scenarios: a Deep Generative Approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"math.OC","authors_text":"Congmei Jiang, Mingbiao Yu, Yi Chai, Yize Chen, Yongfang Mao","submitted_at":"2019-03-13T01:11:39Z","abstract_excerpt":"The operation and planning of large-scale power systems are becoming more challenging with the increasing penetration of stochastic renewable generation. In order to minimize the decision risks in power systems with large amount of renewable resources, there is a growing need to model the short-term generation uncertainty. By producing a group of possible future realizations for certain set of renewable generation plants, scenario approach has become one popular way for renewables uncertainty modeling. However, due to the complex spatial and temporal correlations underlying in renewable genera"},"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":"1903.05274","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2019-03-13T01:11:39Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"72f5d417221e7579cd3d64517e84cf897fbccb0834e64f2df01b79c0c9418ed8","abstract_canon_sha256":"8a55f241b7ebfdcba945dba18e5430aacf3adb0649b9dcbac78bddbd26875acd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:51:20.992654Z","signature_b64":"gOJ+yWlP1rVk4wx8Enufjwnp+0d0+LcsxBdYq8Yoc3CRuZvjrvLEdHl9NCSOUBqQYSEewCJsUgLI8tWR+MGXAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fdb37be3d397f88116479155c298d7a395cf7f073ac4022ac8044b1a4058a3f8","last_reissued_at":"2026-05-17T23:51:20.991997Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:51:20.991997Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Forecasting Spatio-Temporal Renewable Scenarios: a Deep Generative Approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"math.OC","authors_text":"Congmei Jiang, Mingbiao Yu, Yi Chai, Yize Chen, Yongfang Mao","submitted_at":"2019-03-13T01:11:39Z","abstract_excerpt":"The operation and planning of large-scale power systems are becoming more challenging with the increasing penetration of stochastic renewable generation. In order to minimize the decision risks in power systems with large amount of renewable resources, there is a growing need to model the short-term generation uncertainty. By producing a group of possible future realizations for certain set of renewable generation plants, scenario approach has become one popular way for renewables uncertainty modeling. However, due to the complex spatial and temporal correlations underlying in renewable genera"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.05274","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":"1903.05274","created_at":"2026-05-17T23:51:20.992101+00:00"},{"alias_kind":"arxiv_version","alias_value":"1903.05274v1","created_at":"2026-05-17T23:51:20.992101+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.05274","created_at":"2026-05-17T23:51:20.992101+00:00"},{"alias_kind":"pith_short_12","alias_value":"7WZXXY6TS74I","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_16","alias_value":"7WZXXY6TS74ICFSH","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_8","alias_value":"7WZXXY6T","created_at":"2026-05-18T12:33:12.712433+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/7WZXXY6TS74ICFSHSFK4FGGXUO","json":"https://pith.science/pith/7WZXXY6TS74ICFSHSFK4FGGXUO.json","graph_json":"https://pith.science/api/pith-number/7WZXXY6TS74ICFSHSFK4FGGXUO/graph.json","events_json":"https://pith.science/api/pith-number/7WZXXY6TS74ICFSHSFK4FGGXUO/events.json","paper":"https://pith.science/paper/7WZXXY6T"},"agent_actions":{"view_html":"https://pith.science/pith/7WZXXY6TS74ICFSHSFK4FGGXUO","download_json":"https://pith.science/pith/7WZXXY6TS74ICFSHSFK4FGGXUO.json","view_paper":"https://pith.science/paper/7WZXXY6T","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1903.05274&json=true","fetch_graph":"https://pith.science/api/pith-number/7WZXXY6TS74ICFSHSFK4FGGXUO/graph.json","fetch_events":"https://pith.science/api/pith-number/7WZXXY6TS74ICFSHSFK4FGGXUO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7WZXXY6TS74ICFSHSFK4FGGXUO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7WZXXY6TS74ICFSHSFK4FGGXUO/action/storage_attestation","attest_author":"https://pith.science/pith/7WZXXY6TS74ICFSHSFK4FGGXUO/action/author_attestation","sign_citation":"https://pith.science/pith/7WZXXY6TS74ICFSHSFK4FGGXUO/action/citation_signature","submit_replication":"https://pith.science/pith/7WZXXY6TS74ICFSHSFK4FGGXUO/action/replication_record"}},"created_at":"2026-05-17T23:51:20.992101+00:00","updated_at":"2026-05-17T23:51:20.992101+00:00"}