{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:MZXNA6EKMZKTHUTO2CAAFNFSJF","short_pith_number":"pith:MZXNA6EK","schema_version":"1.0","canonical_sha256":"666ed0788a665533d26ed08002b4b24978768e27eb7dd9e27fe788e3e76993e6","source":{"kind":"arxiv","id":"2204.13939","version":3},"attestation_state":"computed","paper":{"title":"Short-Term Density Forecasting of Low-Voltage Load using Bernstein-Polynomial Normalizing Flows","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP","stat.ME","stat.ML"],"primary_cat":"cs.LG","authors_text":"Beate Sick, Marcel Arpogaus, Marcus Voss, Mark Nigge-Uricher, Oliver D\\\"urr","submitted_at":"2022-04-29T08:32:02Z","abstract_excerpt":"The transition to a fully renewable energy grid requires better forecasting of demand at the low-voltage level to increase efficiency and ensure reliable control. However, high fluctuations and increasing electrification cause huge forecast variability, not reflected in traditional point estimates. Probabilistic load forecasts take future uncertainties into account and thus allow more informed decision-making for the planning and operation of low-carbon energy systems. We propose an approach for flexible conditional density forecasting of short-term load based on Bernstein polynomial normalizi"},"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":"2204.13939","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-04-29T08:32:02Z","cross_cats_sorted":["stat.AP","stat.ME","stat.ML"],"title_canon_sha256":"967e642be74b219994a597d6ba26b562977fea8194e446501057341dba68e838","abstract_canon_sha256":"4088f787729e268399ddf5c10369e39d6413a937a4d16bbd3ed1ac072e0a336a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:21:03.395707Z","signature_b64":"MRkZ6ni0l7dD3tb5aMmVqOVzqk9SXvlvV6cqZOV9CtYfqJwdhkxmF8qwUkiCgZ9zOjYlaW+rZezGe1OkbyKkCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"666ed0788a665533d26ed08002b4b24978768e27eb7dd9e27fe788e3e76993e6","last_reissued_at":"2026-07-05T06:21:03.395260Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:21:03.395260Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Short-Term Density Forecasting of Low-Voltage Load using Bernstein-Polynomial Normalizing Flows","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP","stat.ME","stat.ML"],"primary_cat":"cs.LG","authors_text":"Beate Sick, Marcel Arpogaus, Marcus Voss, Mark Nigge-Uricher, Oliver D\\\"urr","submitted_at":"2022-04-29T08:32:02Z","abstract_excerpt":"The transition to a fully renewable energy grid requires better forecasting of demand at the low-voltage level to increase efficiency and ensure reliable control. However, high fluctuations and increasing electrification cause huge forecast variability, not reflected in traditional point estimates. Probabilistic load forecasts take future uncertainties into account and thus allow more informed decision-making for the planning and operation of low-carbon energy systems. We propose an approach for flexible conditional density forecasting of short-term load based on Bernstein polynomial normalizi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2204.13939","kind":"arxiv","version":3},"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/2204.13939/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":"2204.13939","created_at":"2026-07-05T06:21:03.395326+00:00"},{"alias_kind":"arxiv_version","alias_value":"2204.13939v3","created_at":"2026-07-05T06:21:03.395326+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2204.13939","created_at":"2026-07-05T06:21:03.395326+00:00"},{"alias_kind":"pith_short_12","alias_value":"MZXNA6EKMZKT","created_at":"2026-07-05T06:21:03.395326+00:00"},{"alias_kind":"pith_short_16","alias_value":"MZXNA6EKMZKTHUTO","created_at":"2026-07-05T06:21:03.395326+00:00"},{"alias_kind":"pith_short_8","alias_value":"MZXNA6EK","created_at":"2026-07-05T06:21:03.395326+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.20253","citing_title":"Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios","ref_index":77,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MZXNA6EKMZKTHUTO2CAAFNFSJF","json":"https://pith.science/pith/MZXNA6EKMZKTHUTO2CAAFNFSJF.json","graph_json":"https://pith.science/api/pith-number/MZXNA6EKMZKTHUTO2CAAFNFSJF/graph.json","events_json":"https://pith.science/api/pith-number/MZXNA6EKMZKTHUTO2CAAFNFSJF/events.json","paper":"https://pith.science/paper/MZXNA6EK"},"agent_actions":{"view_html":"https://pith.science/pith/MZXNA6EKMZKTHUTO2CAAFNFSJF","download_json":"https://pith.science/pith/MZXNA6EKMZKTHUTO2CAAFNFSJF.json","view_paper":"https://pith.science/paper/MZXNA6EK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2204.13939&json=true","fetch_graph":"https://pith.science/api/pith-number/MZXNA6EKMZKTHUTO2CAAFNFSJF/graph.json","fetch_events":"https://pith.science/api/pith-number/MZXNA6EKMZKTHUTO2CAAFNFSJF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MZXNA6EKMZKTHUTO2CAAFNFSJF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MZXNA6EKMZKTHUTO2CAAFNFSJF/action/storage_attestation","attest_author":"https://pith.science/pith/MZXNA6EKMZKTHUTO2CAAFNFSJF/action/author_attestation","sign_citation":"https://pith.science/pith/MZXNA6EKMZKTHUTO2CAAFNFSJF/action/citation_signature","submit_replication":"https://pith.science/pith/MZXNA6EKMZKTHUTO2CAAFNFSJF/action/replication_record"}},"created_at":"2026-07-05T06:21:03.395326+00:00","updated_at":"2026-07-05T06:21:03.395326+00:00"}