{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:MC2I5HJDGZTYTMV4Y6KI5UBMFN","short_pith_number":"pith:MC2I5HJD","canonical_record":{"source":{"id":"2107.13653","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2021-07-28T21:45:54Z","cross_cats_sorted":[],"title_canon_sha256":"4f2ace133272b6357e954d03f8daecb1e0f714ad02bf1629e75bbed5461682db","abstract_canon_sha256":"2588dddebafc0aae5dd0fec34c5ed21875e79a98646af0290b6eeeff2cd86586"},"schema_version":"1.0"},"canonical_sha256":"60b48e9d23366789b2bcc7948ed02c2b769f69ff334c4c4b89152f874a4481c3","source":{"kind":"arxiv","id":"2107.13653","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2107.13653","created_at":"2026-07-05T03:01:38Z"},{"alias_kind":"arxiv_version","alias_value":"2107.13653v1","created_at":"2026-07-05T03:01:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2107.13653","created_at":"2026-07-05T03:01:38Z"},{"alias_kind":"pith_short_12","alias_value":"MC2I5HJDGZTY","created_at":"2026-07-05T03:01:38Z"},{"alias_kind":"pith_short_16","alias_value":"MC2I5HJDGZTYTMV4","created_at":"2026-07-05T03:01:38Z"},{"alias_kind":"pith_short_8","alias_value":"MC2I5HJD","created_at":"2026-07-05T03:01:38Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:MC2I5HJDGZTYTMV4Y6KI5UBMFN","target":"record","payload":{"canonical_record":{"source":{"id":"2107.13653","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2021-07-28T21:45:54Z","cross_cats_sorted":[],"title_canon_sha256":"4f2ace133272b6357e954d03f8daecb1e0f714ad02bf1629e75bbed5461682db","abstract_canon_sha256":"2588dddebafc0aae5dd0fec34c5ed21875e79a98646af0290b6eeeff2cd86586"},"schema_version":"1.0"},"canonical_sha256":"60b48e9d23366789b2bcc7948ed02c2b769f69ff334c4c4b89152f874a4481c3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:01:38.868195Z","signature_b64":"v3Fzx5m6r15EuLp4f0VbeGSey3NMOQcEyAnreP3vu42R4P3GDs2/aLfhk8ESxEizFtZpxvJIWcLR2XcHGJ2VCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"60b48e9d23366789b2bcc7948ed02c2b769f69ff334c4c4b89152f874a4481c3","last_reissued_at":"2026-07-05T03:01:38.867796Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:01:38.867796Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2107.13653","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T03:01:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Dmmoq04/zKmCb/6+NdYgCFIZbI4ERwZqCvKqMUIin88sHqknbARaK4ECP/uEDNPeIbKDTyOtmxH5ShLhbu42Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-22T16:11:11.164308Z"},"content_sha256":"9782b76ce6c4bed1ac93f428c50664c89d2b1ab17515314e15dc717b776049e5","schema_version":"1.0","event_id":"sha256:9782b76ce6c4bed1ac93f428c50664c89d2b1ab17515314e15dc717b776049e5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:MC2I5HJDGZTYTMV4Y6KI5UBMFN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Demand Forecasting in Smart Grid Using Long Short-Term Memory","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Abtahi Ishmam, Kazi Abu Taher, Koushik Roy","submitted_at":"2021-07-28T21:45:54Z","abstract_excerpt":"Demand forecasting in power sector has become an important part of modern demand management and response systems with the rise of smart metering enabled grids. Long Short-Term Memory (LSTM) shows promising results in predicting time series data which can also be applied to power load demand in smart grids. In this paper, an LSTM based model using neural network architecture is proposed to forecast power demand. The model is trained with hourly energy and power usage data of four years from a smart grid. After training and prediction, the accuracy of the model is compared against the traditiona"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2107.13653","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/2107.13653/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T03:01:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GMjLT1QUeXmP+h/6MypUybj6IYMdMiJniYfH+/1um4rvR6rxQ8URLbIrQKPh8LKUExoVTP9FQ3XBs8+CbTe2BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-22T16:11:11.164683Z"},"content_sha256":"c2e36c7759ab1baada957773fa6ce413e978f15e1650c773cf1377ed3d1a7a33","schema_version":"1.0","event_id":"sha256:c2e36c7759ab1baada957773fa6ce413e978f15e1650c773cf1377ed3d1a7a33"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MC2I5HJDGZTYTMV4Y6KI5UBMFN/bundle.json","state_url":"https://pith.science/pith/MC2I5HJDGZTYTMV4Y6KI5UBMFN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MC2I5HJDGZTYTMV4Y6KI5UBMFN/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-22T16:11:11Z","links":{"resolver":"https://pith.science/pith/MC2I5HJDGZTYTMV4Y6KI5UBMFN","bundle":"https://pith.science/pith/MC2I5HJDGZTYTMV4Y6KI5UBMFN/bundle.json","state":"https://pith.science/pith/MC2I5HJDGZTYTMV4Y6KI5UBMFN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MC2I5HJDGZTYTMV4Y6KI5UBMFN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:MC2I5HJDGZTYTMV4Y6KI5UBMFN","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"2588dddebafc0aae5dd0fec34c5ed21875e79a98646af0290b6eeeff2cd86586","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2021-07-28T21:45:54Z","title_canon_sha256":"4f2ace133272b6357e954d03f8daecb1e0f714ad02bf1629e75bbed5461682db"},"schema_version":"1.0","source":{"id":"2107.13653","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2107.13653","created_at":"2026-07-05T03:01:38Z"},{"alias_kind":"arxiv_version","alias_value":"2107.13653v1","created_at":"2026-07-05T03:01:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2107.13653","created_at":"2026-07-05T03:01:38Z"},{"alias_kind":"pith_short_12","alias_value":"MC2I5HJDGZTY","created_at":"2026-07-05T03:01:38Z"},{"alias_kind":"pith_short_16","alias_value":"MC2I5HJDGZTYTMV4","created_at":"2026-07-05T03:01:38Z"},{"alias_kind":"pith_short_8","alias_value":"MC2I5HJD","created_at":"2026-07-05T03:01:38Z"}],"graph_snapshots":[{"event_id":"sha256:c2e36c7759ab1baada957773fa6ce413e978f15e1650c773cf1377ed3d1a7a33","target":"graph","created_at":"2026-07-05T03:01:38Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2107.13653/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Demand forecasting in power sector has become an important part of modern demand management and response systems with the rise of smart metering enabled grids. Long Short-Term Memory (LSTM) shows promising results in predicting time series data which can also be applied to power load demand in smart grids. In this paper, an LSTM based model using neural network architecture is proposed to forecast power demand. The model is trained with hourly energy and power usage data of four years from a smart grid. After training and prediction, the accuracy of the model is compared against the traditiona","authors_text":"Abtahi Ishmam, Kazi Abu Taher, Koushik Roy","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2021-07-28T21:45:54Z","title":"Demand Forecasting in Smart Grid Using Long Short-Term Memory"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2107.13653","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:9782b76ce6c4bed1ac93f428c50664c89d2b1ab17515314e15dc717b776049e5","target":"record","created_at":"2026-07-05T03:01:38Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"2588dddebafc0aae5dd0fec34c5ed21875e79a98646af0290b6eeeff2cd86586","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2021-07-28T21:45:54Z","title_canon_sha256":"4f2ace133272b6357e954d03f8daecb1e0f714ad02bf1629e75bbed5461682db"},"schema_version":"1.0","source":{"id":"2107.13653","kind":"arxiv","version":1}},"canonical_sha256":"60b48e9d23366789b2bcc7948ed02c2b769f69ff334c4c4b89152f874a4481c3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"60b48e9d23366789b2bcc7948ed02c2b769f69ff334c4c4b89152f874a4481c3","first_computed_at":"2026-07-05T03:01:38.867796Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:01:38.867796Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"v3Fzx5m6r15EuLp4f0VbeGSey3NMOQcEyAnreP3vu42R4P3GDs2/aLfhk8ESxEizFtZpxvJIWcLR2XcHGJ2VCw==","signature_status":"signed_v1","signed_at":"2026-07-05T03:01:38.868195Z","signed_message":"canonical_sha256_bytes"},"source_id":"2107.13653","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9782b76ce6c4bed1ac93f428c50664c89d2b1ab17515314e15dc717b776049e5","sha256:c2e36c7759ab1baada957773fa6ce413e978f15e1650c773cf1377ed3d1a7a33"],"state_sha256":"5189ee0653c82e2e58b3be2a7400c772765a0243017e3efa41c446583062f374"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qIAfZRZ2TfnaTdhV7aIqLslZHSeDvrchdJP4SocZSYBUUi4pCLA68EXTVbFTfBEIWYeErRX/4t6SZqhe7eQGAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-22T16:11:11.166760Z","bundle_sha256":"10caacf24a19bce8be8fd73ede5d5d425b05a632f8963eba5dba43b2caae80a3"}}