{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:IGMBP3OFQBJ6HQ2ARYYYMIGHYI","short_pith_number":"pith:IGMBP3OF","schema_version":"1.0","canonical_sha256":"419817edc58053e3c3408e318620c7c23b0d46d84d1898c3da4dc64081e83790","source":{"kind":"arxiv","id":"2407.08434","version":1},"attestation_state":"computed","paper":{"title":"Improve Load Forecasting in Energy Communities through Transfer Learning using Open-Access Synthetic Profiles","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Gerhard Huber, Lukas Moosbrugger, Peter Kepplinger, Valentin Seiler","submitted_at":"2024-07-11T12:17:31Z","abstract_excerpt":"According to a conservative estimate, a 1% reduction in forecast error for a 10 GW energy utility can save up to $ 1.6 million annually. In our context, achieving precise forecasts of future power consumption is crucial for operating flexible energy assets using model predictive control approaches. Specifically, this work focuses on the load profile forecast of a first-year energy community with the common practical challenge of limited historical data availability. We propose to pre-train the load prediction models with open-access synthetic load profiles using transfer learning techniques to"},"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":"2407.08434","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-07-11T12:17:31Z","cross_cats_sorted":[],"title_canon_sha256":"23a395fa91f9ee269e83640ccfc4942c97caa6a1a6296ecbad5f37c8d6df94c3","abstract_canon_sha256":"2ed759969650aaf18c9108717cbcbfa6ab427071f4bca22674101134fbe82f34"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:42:48.547557Z","signature_b64":"sIOVBvUMwT8WK+S3Gz5SsH0E56LJ8zy/+SDjxqerT29yl4zVMB2lo1417ZLoLOyKWA4KtQ8tYmEearqP3zitBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"419817edc58053e3c3408e318620c7c23b0d46d84d1898c3da4dc64081e83790","last_reissued_at":"2026-07-05T08:42:48.547057Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:42:48.547057Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Improve Load Forecasting in Energy Communities through Transfer Learning using Open-Access Synthetic Profiles","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Gerhard Huber, Lukas Moosbrugger, Peter Kepplinger, Valentin Seiler","submitted_at":"2024-07-11T12:17:31Z","abstract_excerpt":"According to a conservative estimate, a 1% reduction in forecast error for a 10 GW energy utility can save up to $ 1.6 million annually. In our context, achieving precise forecasts of future power consumption is crucial for operating flexible energy assets using model predictive control approaches. Specifically, this work focuses on the load profile forecast of a first-year energy community with the common practical challenge of limited historical data availability. We propose to pre-train the load prediction models with open-access synthetic load profiles using transfer learning techniques to"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.08434","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/2407.08434/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":"2407.08434","created_at":"2026-07-05T08:42:48.547124+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.08434v1","created_at":"2026-07-05T08:42:48.547124+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.08434","created_at":"2026-07-05T08:42:48.547124+00:00"},{"alias_kind":"pith_short_12","alias_value":"IGMBP3OFQBJ6","created_at":"2026-07-05T08:42:48.547124+00:00"},{"alias_kind":"pith_short_16","alias_value":"IGMBP3OFQBJ6HQ2A","created_at":"2026-07-05T08:42:48.547124+00:00"},{"alias_kind":"pith_short_8","alias_value":"IGMBP3OF","created_at":"2026-07-05T08:42:48.547124+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.05000","citing_title":"Load Forecasting for Households and Energy Communities: Are Deep Learning Models Worth the Effort?","ref_index":25,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/IGMBP3OFQBJ6HQ2ARYYYMIGHYI","json":"https://pith.science/pith/IGMBP3OFQBJ6HQ2ARYYYMIGHYI.json","graph_json":"https://pith.science/api/pith-number/IGMBP3OFQBJ6HQ2ARYYYMIGHYI/graph.json","events_json":"https://pith.science/api/pith-number/IGMBP3OFQBJ6HQ2ARYYYMIGHYI/events.json","paper":"https://pith.science/paper/IGMBP3OF"},"agent_actions":{"view_html":"https://pith.science/pith/IGMBP3OFQBJ6HQ2ARYYYMIGHYI","download_json":"https://pith.science/pith/IGMBP3OFQBJ6HQ2ARYYYMIGHYI.json","view_paper":"https://pith.science/paper/IGMBP3OF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.08434&json=true","fetch_graph":"https://pith.science/api/pith-number/IGMBP3OFQBJ6HQ2ARYYYMIGHYI/graph.json","fetch_events":"https://pith.science/api/pith-number/IGMBP3OFQBJ6HQ2ARYYYMIGHYI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IGMBP3OFQBJ6HQ2ARYYYMIGHYI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IGMBP3OFQBJ6HQ2ARYYYMIGHYI/action/storage_attestation","attest_author":"https://pith.science/pith/IGMBP3OFQBJ6HQ2ARYYYMIGHYI/action/author_attestation","sign_citation":"https://pith.science/pith/IGMBP3OFQBJ6HQ2ARYYYMIGHYI/action/citation_signature","submit_replication":"https://pith.science/pith/IGMBP3OFQBJ6HQ2ARYYYMIGHYI/action/replication_record"}},"created_at":"2026-07-05T08:42:48.547124+00:00","updated_at":"2026-07-05T08:42:48.547124+00:00"}