{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:6JBVUHT6E7AOEEAJLX4V2PSGZY","short_pith_number":"pith:6JBVUHT6","schema_version":"1.0","canonical_sha256":"f2435a1e7e27c0e210095df95d3e46ce39c4e9feea61d278f48de3c36866f64b","source":{"kind":"arxiv","id":"2207.00913","version":1},"attestation_state":"computed","paper":{"title":"SKIPP'D: a SKy Images and Photovoltaic Power Generation Dataset for Short-term Solar Forecasting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Adam Brandt, Andea Scott, Vignesh Venugopal, Xiatong Li, Yuchi Sun, Yuhao Nie","submitted_at":"2022-07-02T21:52:50Z","abstract_excerpt":"Large-scale integration of photovoltaics (PV) into electricity grids is challenged by the intermittent nature of solar power. Sky-image-based solar forecasting using deep learning has been recognized as a promising approach to predicting the short-term fluctuations. However, there are few publicly available standardized benchmark datasets for image-based solar forecasting, which limits the comparison of different forecasting models and the exploration of forecasting methods. To fill these gaps, we introduce SKIPP'D -- a SKy Images and Photovoltaic Power Generation Dataset. The dataset contains"},"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":"2207.00913","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-07-02T21:52:50Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"0459f281b1c75bc3372d104bb461f54f61607fbb0e71a970413a082b8b686881","abstract_canon_sha256":"47c6061e172fde239b4efe6e180ef126bebece5bf3e0b9ec30ec158932aa706d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:36:53.989600Z","signature_b64":"n3vF+Tz30yfhtYQhaZ3rqjpMAXz5qISYS+cI97i5CQ/yFjI6k71HdgyJn+IU+oO5hsCCW6QV95G5zcDDagPUDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f2435a1e7e27c0e210095df95d3e46ce39c4e9feea61d278f48de3c36866f64b","last_reissued_at":"2026-07-05T04:36:53.989208Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:36:53.989208Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SKIPP'D: a SKy Images and Photovoltaic Power Generation Dataset for Short-term Solar Forecasting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Adam Brandt, Andea Scott, Vignesh Venugopal, Xiatong Li, Yuchi Sun, Yuhao Nie","submitted_at":"2022-07-02T21:52:50Z","abstract_excerpt":"Large-scale integration of photovoltaics (PV) into electricity grids is challenged by the intermittent nature of solar power. Sky-image-based solar forecasting using deep learning has been recognized as a promising approach to predicting the short-term fluctuations. However, there are few publicly available standardized benchmark datasets for image-based solar forecasting, which limits the comparison of different forecasting models and the exploration of forecasting methods. To fill these gaps, we introduce SKIPP'D -- a SKy Images and Photovoltaic Power Generation Dataset. The dataset contains"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.00913","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/2207.00913/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":"2207.00913","created_at":"2026-07-05T04:36:53.989266+00:00"},{"alias_kind":"arxiv_version","alias_value":"2207.00913v1","created_at":"2026-07-05T04:36:53.989266+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.00913","created_at":"2026-07-05T04:36:53.989266+00:00"},{"alias_kind":"pith_short_12","alias_value":"6JBVUHT6E7AO","created_at":"2026-07-05T04:36:53.989266+00:00"},{"alias_kind":"pith_short_16","alias_value":"6JBVUHT6E7AOEEAJ","created_at":"2026-07-05T04:36:53.989266+00:00"},{"alias_kind":"pith_short_8","alias_value":"6JBVUHT6","created_at":"2026-07-05T04:36:53.989266+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/6JBVUHT6E7AOEEAJLX4V2PSGZY","json":"https://pith.science/pith/6JBVUHT6E7AOEEAJLX4V2PSGZY.json","graph_json":"https://pith.science/api/pith-number/6JBVUHT6E7AOEEAJLX4V2PSGZY/graph.json","events_json":"https://pith.science/api/pith-number/6JBVUHT6E7AOEEAJLX4V2PSGZY/events.json","paper":"https://pith.science/paper/6JBVUHT6"},"agent_actions":{"view_html":"https://pith.science/pith/6JBVUHT6E7AOEEAJLX4V2PSGZY","download_json":"https://pith.science/pith/6JBVUHT6E7AOEEAJLX4V2PSGZY.json","view_paper":"https://pith.science/paper/6JBVUHT6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2207.00913&json=true","fetch_graph":"https://pith.science/api/pith-number/6JBVUHT6E7AOEEAJLX4V2PSGZY/graph.json","fetch_events":"https://pith.science/api/pith-number/6JBVUHT6E7AOEEAJLX4V2PSGZY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6JBVUHT6E7AOEEAJLX4V2PSGZY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6JBVUHT6E7AOEEAJLX4V2PSGZY/action/storage_attestation","attest_author":"https://pith.science/pith/6JBVUHT6E7AOEEAJLX4V2PSGZY/action/author_attestation","sign_citation":"https://pith.science/pith/6JBVUHT6E7AOEEAJLX4V2PSGZY/action/citation_signature","submit_replication":"https://pith.science/pith/6JBVUHT6E7AOEEAJLX4V2PSGZY/action/replication_record"}},"created_at":"2026-07-05T04:36:53.989266+00:00","updated_at":"2026-07-05T04:36:53.989266+00:00"}