{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:ZWXRLJJ526ACV2AYZRG47YEG2D","short_pith_number":"pith:ZWXRLJJ5","schema_version":"1.0","canonical_sha256":"cdaf15a53dd7802ae818cc4dcfe086d0fdbb67c62f1139a68670f207d6f671e7","source":{"kind":"arxiv","id":"1810.04642","version":1},"attestation_state":"computed","paper":{"title":"Virtual Battery Parameter Identification using Transfer Learning based Stacked Autoencoder","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Indrasis Chakraborty, Sai Pushpak Nandanoori, Soumya Kundu","submitted_at":"2018-10-10T17:07:53Z","abstract_excerpt":"Recent studies have shown that the aggregated dynamic flexibility of an ensemble of thermostatic loads can be modeled in the form of a virtual battery. The existing methods for computing the virtual battery parameters require the knowledge of the first-principle models and parameter values of the loads in the ensemble. In real-world applications, however, it is likely that the only available information are end-use measurements such as power consumption, room temperature, device on/off status, etc., while very little about the individual load models and parameters are known. We propose a trans"},"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":"1810.04642","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-10-10T17:07:53Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"b18b0352fc3f81a96d7ed1b62e86fffc57a1efe36aa9cfd075d5547c7cfe7b62","abstract_canon_sha256":"5a9f432ad3f1fededb399e018b6347eb5b588a304d329437137a82f888907624"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:03:37.780634Z","signature_b64":"a+OKuHPYxam2xQjrcxlOSfe3uPR8JMNPq7bULGP1DPnxRpc6CsyHjD6SDxBekoUnqO3oPNbiS2M7tu0d5x5JAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cdaf15a53dd7802ae818cc4dcfe086d0fdbb67c62f1139a68670f207d6f671e7","last_reissued_at":"2026-05-18T00:03:37.779990Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:03:37.779990Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Virtual Battery Parameter Identification using Transfer Learning based Stacked Autoencoder","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Indrasis Chakraborty, Sai Pushpak Nandanoori, Soumya Kundu","submitted_at":"2018-10-10T17:07:53Z","abstract_excerpt":"Recent studies have shown that the aggregated dynamic flexibility of an ensemble of thermostatic loads can be modeled in the form of a virtual battery. The existing methods for computing the virtual battery parameters require the knowledge of the first-principle models and parameter values of the loads in the ensemble. In real-world applications, however, it is likely that the only available information are end-use measurements such as power consumption, room temperature, device on/off status, etc., while very little about the individual load models and parameters are known. We propose a trans"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.04642","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":""},"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":"1810.04642","created_at":"2026-05-18T00:03:37.780089+00:00"},{"alias_kind":"arxiv_version","alias_value":"1810.04642v1","created_at":"2026-05-18T00:03:37.780089+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.04642","created_at":"2026-05-18T00:03:37.780089+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZWXRLJJ526AC","created_at":"2026-05-18T12:33:07.085635+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZWXRLJJ526ACV2AY","created_at":"2026-05-18T12:33:07.085635+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZWXRLJJ5","created_at":"2026-05-18T12:33:07.085635+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/ZWXRLJJ526ACV2AYZRG47YEG2D","json":"https://pith.science/pith/ZWXRLJJ526ACV2AYZRG47YEG2D.json","graph_json":"https://pith.science/api/pith-number/ZWXRLJJ526ACV2AYZRG47YEG2D/graph.json","events_json":"https://pith.science/api/pith-number/ZWXRLJJ526ACV2AYZRG47YEG2D/events.json","paper":"https://pith.science/paper/ZWXRLJJ5"},"agent_actions":{"view_html":"https://pith.science/pith/ZWXRLJJ526ACV2AYZRG47YEG2D","download_json":"https://pith.science/pith/ZWXRLJJ526ACV2AYZRG47YEG2D.json","view_paper":"https://pith.science/paper/ZWXRLJJ5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1810.04642&json=true","fetch_graph":"https://pith.science/api/pith-number/ZWXRLJJ526ACV2AYZRG47YEG2D/graph.json","fetch_events":"https://pith.science/api/pith-number/ZWXRLJJ526ACV2AYZRG47YEG2D/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZWXRLJJ526ACV2AYZRG47YEG2D/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZWXRLJJ526ACV2AYZRG47YEG2D/action/storage_attestation","attest_author":"https://pith.science/pith/ZWXRLJJ526ACV2AYZRG47YEG2D/action/author_attestation","sign_citation":"https://pith.science/pith/ZWXRLJJ526ACV2AYZRG47YEG2D/action/citation_signature","submit_replication":"https://pith.science/pith/ZWXRLJJ526ACV2AYZRG47YEG2D/action/replication_record"}},"created_at":"2026-05-18T00:03:37.780089+00:00","updated_at":"2026-05-18T00:03:37.780089+00:00"}