{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:O4KEE3AW5JLWUYXKAA3D7HDCGM","short_pith_number":"pith:O4KEE3AW","schema_version":"1.0","canonical_sha256":"7714426c16ea576a62ea00363f9c623339444eb4d0b8aceebffee405797194ba","source":{"kind":"arxiv","id":"2506.04294","version":1},"attestation_state":"computed","paper":{"title":"Short-Term Power Demand Forecasting for Diverse Consumer Types to Enhance Grid Planning and Synchronisation","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Ane M. Florez-Tapia, Asier Diaz-Iglesias, Xabier Belaunzaran","submitted_at":"2025-06-04T12:01:11Z","abstract_excerpt":"Ensuring grid stability in the transition to renewable energy sources requires accurate power demand forecasting. This study addresses the need for precise forecasting by differentiating among industrial, commercial, and residential consumers through customer clusterisation, tailoring the forecasting models to capture the unique consumption patterns of each group. A feature selection process is done for each consumer type including temporal, socio-economic, and weather-related data obtained from the Copernicus Earth Observation (EO) program. A variety of AI and machine learning algorithms for "},"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":"2506.04294","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-04T12:01:11Z","cross_cats_sorted":[],"title_canon_sha256":"38b395158b07aaff89e40de6ca86a804b249b2a72c7e60d6aaeb4849ba550222","abstract_canon_sha256":"5a181f107e296c076954c9daab95af1bb249b5a8a67fef898094f9f753e1474d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:16:11.918361Z","signature_b64":"0WH37eMR/8RkMHeX8TYz3d7dxAkdw5kvQbAJoBnku+uT1p4FlVwUHAmLumyr9H2y4/TM1r4iKR1UfByGxzpXBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7714426c16ea576a62ea00363f9c623339444eb4d0b8aceebffee405797194ba","last_reissued_at":"2026-07-05T11:16:11.917858Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:16:11.917858Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Short-Term Power Demand Forecasting for Diverse Consumer Types to Enhance Grid Planning and Synchronisation","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Ane M. Florez-Tapia, Asier Diaz-Iglesias, Xabier Belaunzaran","submitted_at":"2025-06-04T12:01:11Z","abstract_excerpt":"Ensuring grid stability in the transition to renewable energy sources requires accurate power demand forecasting. This study addresses the need for precise forecasting by differentiating among industrial, commercial, and residential consumers through customer clusterisation, tailoring the forecasting models to capture the unique consumption patterns of each group. A feature selection process is done for each consumer type including temporal, socio-economic, and weather-related data obtained from the Copernicus Earth Observation (EO) program. A variety of AI and machine learning algorithms for "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.04294","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2506.04294/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":"2506.04294","created_at":"2026-07-05T11:16:11.917912+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.04294v1","created_at":"2026-07-05T11:16:11.917912+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.04294","created_at":"2026-07-05T11:16:11.917912+00:00"},{"alias_kind":"pith_short_12","alias_value":"O4KEE3AW5JLW","created_at":"2026-07-05T11:16:11.917912+00:00"},{"alias_kind":"pith_short_16","alias_value":"O4KEE3AW5JLWUYXK","created_at":"2026-07-05T11:16:11.917912+00:00"},{"alias_kind":"pith_short_8","alias_value":"O4KEE3AW","created_at":"2026-07-05T11:16:11.917912+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2512.15232","citing_title":"A Blind Source Separation Framework to Monitor Sectoral Power Demand from Grid-Scale Load Measurements","ref_index":18,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/O4KEE3AW5JLWUYXKAA3D7HDCGM","json":"https://pith.science/pith/O4KEE3AW5JLWUYXKAA3D7HDCGM.json","graph_json":"https://pith.science/api/pith-number/O4KEE3AW5JLWUYXKAA3D7HDCGM/graph.json","events_json":"https://pith.science/api/pith-number/O4KEE3AW5JLWUYXKAA3D7HDCGM/events.json","paper":"https://pith.science/paper/O4KEE3AW"},"agent_actions":{"view_html":"https://pith.science/pith/O4KEE3AW5JLWUYXKAA3D7HDCGM","download_json":"https://pith.science/pith/O4KEE3AW5JLWUYXKAA3D7HDCGM.json","view_paper":"https://pith.science/paper/O4KEE3AW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.04294&json=true","fetch_graph":"https://pith.science/api/pith-number/O4KEE3AW5JLWUYXKAA3D7HDCGM/graph.json","fetch_events":"https://pith.science/api/pith-number/O4KEE3AW5JLWUYXKAA3D7HDCGM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/O4KEE3AW5JLWUYXKAA3D7HDCGM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/O4KEE3AW5JLWUYXKAA3D7HDCGM/action/storage_attestation","attest_author":"https://pith.science/pith/O4KEE3AW5JLWUYXKAA3D7HDCGM/action/author_attestation","sign_citation":"https://pith.science/pith/O4KEE3AW5JLWUYXKAA3D7HDCGM/action/citation_signature","submit_replication":"https://pith.science/pith/O4KEE3AW5JLWUYXKAA3D7HDCGM/action/replication_record"}},"created_at":"2026-07-05T11:16:11.917912+00:00","updated_at":"2026-07-05T11:16:11.917912+00:00"}