{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:33XHXBLK5L7XYTOTTZR2FH6F5V","short_pith_number":"pith:33XHXBLK","schema_version":"1.0","canonical_sha256":"deee7b856aeaff7c4dd39e63a29fc5ed79d7615e1bc37af8e1786e0871bade11","source":{"kind":"arxiv","id":"2509.05772","version":1},"attestation_state":"computed","paper":{"title":"Decision-Focused Learning Enhanced by Automated Feature Engineering for Energy Storage Optimisation","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Alexander L. Bowler, Direnc Pekaslan, Isaac Triguero, Ismail Gokay Dogan, Nasser Alkhulaifi, Nicholas J. Watson, Timothy R. Cargan","submitted_at":"2025-09-06T16:54:07Z","abstract_excerpt":"Decision-making under uncertainty in energy management is complicated by unknown parameters hindering optimal strategies, particularly in Battery Energy Storage System (BESS) operations. Predict-Then-Optimise (PTO) approaches treat forecasting and optimisation as separate processes, allowing prediction errors to cascade into suboptimal decisions as models minimise forecasting errors rather than optimising downstream tasks. The emerging Decision-Focused Learning (DFL) methods overcome this limitation by integrating prediction and optimisation; however, they are relatively new and have been test"},"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":"2509.05772","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.AI","submitted_at":"2025-09-06T16:54:07Z","cross_cats_sorted":[],"title_canon_sha256":"f81be96fff76f64c8baec5e48a399a9f5ec3d9d220209fbd558cc4453424cf23","abstract_canon_sha256":"7f0c01ac3ad5bf0b8194b2e18e74ad4ae221104764498100ec1f2982a87f85ab"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:06:23.886985Z","signature_b64":"pHdKQ9a64279/ILq7Hsdtt9Bbl9l4iSs6MQeoxqHkAI6ceX7eWCgrLkj3vKc90quvpnz4gAff9Fo09D/qFhMAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"deee7b856aeaff7c4dd39e63a29fc5ed79d7615e1bc37af8e1786e0871bade11","last_reissued_at":"2026-07-05T12:06:23.886526Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:06:23.886526Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Decision-Focused Learning Enhanced by Automated Feature Engineering for Energy Storage Optimisation","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Alexander L. Bowler, Direnc Pekaslan, Isaac Triguero, Ismail Gokay Dogan, Nasser Alkhulaifi, Nicholas J. Watson, Timothy R. Cargan","submitted_at":"2025-09-06T16:54:07Z","abstract_excerpt":"Decision-making under uncertainty in energy management is complicated by unknown parameters hindering optimal strategies, particularly in Battery Energy Storage System (BESS) operations. Predict-Then-Optimise (PTO) approaches treat forecasting and optimisation as separate processes, allowing prediction errors to cascade into suboptimal decisions as models minimise forecasting errors rather than optimising downstream tasks. The emerging Decision-Focused Learning (DFL) methods overcome this limitation by integrating prediction and optimisation; however, they are relatively new and have been test"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.05772","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/2509.05772/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":"2509.05772","created_at":"2026-07-05T12:06:23.886580+00:00"},{"alias_kind":"arxiv_version","alias_value":"2509.05772v1","created_at":"2026-07-05T12:06:23.886580+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.05772","created_at":"2026-07-05T12:06:23.886580+00:00"},{"alias_kind":"pith_short_12","alias_value":"33XHXBLK5L7X","created_at":"2026-07-05T12:06:23.886580+00:00"},{"alias_kind":"pith_short_16","alias_value":"33XHXBLK5L7XYTOT","created_at":"2026-07-05T12:06:23.886580+00:00"},{"alias_kind":"pith_short_8","alias_value":"33XHXBLK","created_at":"2026-07-05T12:06:23.886580+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/33XHXBLK5L7XYTOTTZR2FH6F5V","json":"https://pith.science/pith/33XHXBLK5L7XYTOTTZR2FH6F5V.json","graph_json":"https://pith.science/api/pith-number/33XHXBLK5L7XYTOTTZR2FH6F5V/graph.json","events_json":"https://pith.science/api/pith-number/33XHXBLK5L7XYTOTTZR2FH6F5V/events.json","paper":"https://pith.science/paper/33XHXBLK"},"agent_actions":{"view_html":"https://pith.science/pith/33XHXBLK5L7XYTOTTZR2FH6F5V","download_json":"https://pith.science/pith/33XHXBLK5L7XYTOTTZR2FH6F5V.json","view_paper":"https://pith.science/paper/33XHXBLK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2509.05772&json=true","fetch_graph":"https://pith.science/api/pith-number/33XHXBLK5L7XYTOTTZR2FH6F5V/graph.json","fetch_events":"https://pith.science/api/pith-number/33XHXBLK5L7XYTOTTZR2FH6F5V/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/33XHXBLK5L7XYTOTTZR2FH6F5V/action/timestamp_anchor","attest_storage":"https://pith.science/pith/33XHXBLK5L7XYTOTTZR2FH6F5V/action/storage_attestation","attest_author":"https://pith.science/pith/33XHXBLK5L7XYTOTTZR2FH6F5V/action/author_attestation","sign_citation":"https://pith.science/pith/33XHXBLK5L7XYTOTTZR2FH6F5V/action/citation_signature","submit_replication":"https://pith.science/pith/33XHXBLK5L7XYTOTTZR2FH6F5V/action/replication_record"}},"created_at":"2026-07-05T12:06:23.886580+00:00","updated_at":"2026-07-05T12:06:23.886580+00:00"}