{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:3OUHO3SVVNZTCAZGYQSRXPMEOH","short_pith_number":"pith:3OUHO3SV","canonical_record":{"source":{"id":"1905.12286","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2019-05-29T09:25:53Z","cross_cats_sorted":[],"title_canon_sha256":"45034265418bc67efcb479ca92b0105d362639898fb784e3d43a9f9f1a1a40bf","abstract_canon_sha256":"acc3954d9a3c2c6d2f69f701350164a86769fa47f505206e6e8dfac333d62b3c"},"schema_version":"1.0"},"canonical_sha256":"dba8776e55ab73310326c4251bbd8471c11bb1f7a4273fd389c81c6bc26c205f","source":{"kind":"arxiv","id":"1905.12286","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.12286","created_at":"2026-05-17T23:44:44Z"},{"alias_kind":"arxiv_version","alias_value":"1905.12286v1","created_at":"2026-05-17T23:44:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.12286","created_at":"2026-05-17T23:44:44Z"},{"alias_kind":"pith_short_12","alias_value":"3OUHO3SVVNZT","created_at":"2026-05-18T12:33:07Z"},{"alias_kind":"pith_short_16","alias_value":"3OUHO3SVVNZTCAZG","created_at":"2026-05-18T12:33:07Z"},{"alias_kind":"pith_short_8","alias_value":"3OUHO3SV","created_at":"2026-05-18T12:33:07Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:3OUHO3SVVNZTCAZGYQSRXPMEOH","target":"record","payload":{"canonical_record":{"source":{"id":"1905.12286","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2019-05-29T09:25:53Z","cross_cats_sorted":[],"title_canon_sha256":"45034265418bc67efcb479ca92b0105d362639898fb784e3d43a9f9f1a1a40bf","abstract_canon_sha256":"acc3954d9a3c2c6d2f69f701350164a86769fa47f505206e6e8dfac333d62b3c"},"schema_version":"1.0"},"canonical_sha256":"dba8776e55ab73310326c4251bbd8471c11bb1f7a4273fd389c81c6bc26c205f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:44:44.750521Z","signature_b64":"GiN4RckMdZYtM0ZShAjgYeowRDKFQMIAEURz6ibeFcX0F1U+0jCko/hGOyBYnkoLG9/gqFwSwOC5gmLZNUrCDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dba8776e55ab73310326c4251bbd8471c11bb1f7a4273fd389c81c6bc26c205f","last_reissued_at":"2026-05-17T23:44:44.749993Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:44:44.749993Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1905.12286","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:44:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"q0TeJSsblfNC0ij/f/RZtsVey1p0t7VMj2QgQOCyis+DzWklnnrR/4Viyc0XBMTaHDCCfxhU+ITcU3jJCQVlCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T01:34:32.643137Z"},"content_sha256":"fa11f3de28e8b9923e648fb39bbc701918e9fb3a4474910cd8643b002cc894d3","schema_version":"1.0","event_id":"sha256:fa11f3de28e8b9923e648fb39bbc701918e9fb3a4474910cd8643b002cc894d3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:3OUHO3SVVNZTCAZGYQSRXPMEOH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Analytical Solution for Stochastic Unit Commitment Considering Wind Power Uncertainty with Gaussian Mixture Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Bin Wang, Mingjie Li, Wenchuan Wu, Yue Yang","submitted_at":"2019-05-29T09:25:53Z","abstract_excerpt":"To capture the stochastic characteristics of renewable energy generation output, the chance-constrained unit commitment (CCUC) model is widely used. Conventionally, analytical solution for CCUC is usually based on simplified probability assumption or neglecting some operational constraints, otherwise scenar-io-based methods are used to approximate probability with heavy computation burden. In this paper, Gaussian mixture model (GMM) is employed to characterize the correlation between wind farms and probability distribution of their forecast errors. In our model, chance constraints including re"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.12286","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:44:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TMlh6nfQmyqIjrO9aDP/U+T5i7LXRBnNPvsBNWwPULO/SLW4eQUXiJs7cw4DBf1Mxh57ZK4qswZgrusnSXVeBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T01:34:32.644035Z"},"content_sha256":"9f3f55a4e91b6560102aeb74535fa70606ffcd3b5f9b8cbe82b0c7f623102e5f","schema_version":"1.0","event_id":"sha256:9f3f55a4e91b6560102aeb74535fa70606ffcd3b5f9b8cbe82b0c7f623102e5f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3OUHO3SVVNZTCAZGYQSRXPMEOH/bundle.json","state_url":"https://pith.science/pith/3OUHO3SVVNZTCAZGYQSRXPMEOH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3OUHO3SVVNZTCAZGYQSRXPMEOH/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-01T01:34:32Z","links":{"resolver":"https://pith.science/pith/3OUHO3SVVNZTCAZGYQSRXPMEOH","bundle":"https://pith.science/pith/3OUHO3SVVNZTCAZGYQSRXPMEOH/bundle.json","state":"https://pith.science/pith/3OUHO3SVVNZTCAZGYQSRXPMEOH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3OUHO3SVVNZTCAZGYQSRXPMEOH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:3OUHO3SVVNZTCAZGYQSRXPMEOH","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"acc3954d9a3c2c6d2f69f701350164a86769fa47f505206e6e8dfac333d62b3c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2019-05-29T09:25:53Z","title_canon_sha256":"45034265418bc67efcb479ca92b0105d362639898fb784e3d43a9f9f1a1a40bf"},"schema_version":"1.0","source":{"id":"1905.12286","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.12286","created_at":"2026-05-17T23:44:44Z"},{"alias_kind":"arxiv_version","alias_value":"1905.12286v1","created_at":"2026-05-17T23:44:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.12286","created_at":"2026-05-17T23:44:44Z"},{"alias_kind":"pith_short_12","alias_value":"3OUHO3SVVNZT","created_at":"2026-05-18T12:33:07Z"},{"alias_kind":"pith_short_16","alias_value":"3OUHO3SVVNZTCAZG","created_at":"2026-05-18T12:33:07Z"},{"alias_kind":"pith_short_8","alias_value":"3OUHO3SV","created_at":"2026-05-18T12:33:07Z"}],"graph_snapshots":[{"event_id":"sha256:9f3f55a4e91b6560102aeb74535fa70606ffcd3b5f9b8cbe82b0c7f623102e5f","target":"graph","created_at":"2026-05-17T23:44:44Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"To capture the stochastic characteristics of renewable energy generation output, the chance-constrained unit commitment (CCUC) model is widely used. Conventionally, analytical solution for CCUC is usually based on simplified probability assumption or neglecting some operational constraints, otherwise scenar-io-based methods are used to approximate probability with heavy computation burden. In this paper, Gaussian mixture model (GMM) is employed to characterize the correlation between wind farms and probability distribution of their forecast errors. In our model, chance constraints including re","authors_text":"Bin Wang, Mingjie Li, Wenchuan Wu, Yue Yang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2019-05-29T09:25:53Z","title":"Analytical Solution for Stochastic Unit Commitment Considering Wind Power Uncertainty with Gaussian Mixture Model"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.12286","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:fa11f3de28e8b9923e648fb39bbc701918e9fb3a4474910cd8643b002cc894d3","target":"record","created_at":"2026-05-17T23:44:44Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"acc3954d9a3c2c6d2f69f701350164a86769fa47f505206e6e8dfac333d62b3c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2019-05-29T09:25:53Z","title_canon_sha256":"45034265418bc67efcb479ca92b0105d362639898fb784e3d43a9f9f1a1a40bf"},"schema_version":"1.0","source":{"id":"1905.12286","kind":"arxiv","version":1}},"canonical_sha256":"dba8776e55ab73310326c4251bbd8471c11bb1f7a4273fd389c81c6bc26c205f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"dba8776e55ab73310326c4251bbd8471c11bb1f7a4273fd389c81c6bc26c205f","first_computed_at":"2026-05-17T23:44:44.749993Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:44:44.749993Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"GiN4RckMdZYtM0ZShAjgYeowRDKFQMIAEURz6ibeFcX0F1U+0jCko/hGOyBYnkoLG9/gqFwSwOC5gmLZNUrCDQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:44:44.750521Z","signed_message":"canonical_sha256_bytes"},"source_id":"1905.12286","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fa11f3de28e8b9923e648fb39bbc701918e9fb3a4474910cd8643b002cc894d3","sha256:9f3f55a4e91b6560102aeb74535fa70606ffcd3b5f9b8cbe82b0c7f623102e5f"],"state_sha256":"ae9e5192236d8e101782f2e2221121b38ad64c885cba4d4d0c4256367d5b0881"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iuXBvVsLToFVsoAr9anB9/kZpR9hgBHOBd9UlBv4BIGsTIgYlCpvs/N3lEDQTmnZ4OR8GJvqxaiKYjAz2URCDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-01T01:34:32.648380Z","bundle_sha256":"c18c1f21a547bcfc26adebb88f3a7311b786055d1746307a24389bc3a7171c64"}}