{"id":"W3187911394","doi":"10.24963/ijcai.2021/374","title":"Residential Electric Load Forecasting via Attentive Transfer of Graph Neural Networks","year":2021,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Artificial neural network; Process (computing); Electrical load; Transfer of learning; Electric power system; Graph; Artificial intelligence; Machine learning; Time series; Transfer (computing); Data mining; Power (physics); Theoretical computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005184651,0.0008198907,0.0005300794,0.0006109832,0.0002799591,0.0004148473,0.001356849,0.000863507,0.001176568],"category_scores_gemma":[0.001654539,0.0003550133,0.0005260727,0.0005534326,0.0004977503,0.001262592,0.0007972228,0.001080421,0.0002430024],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009570797,"about_ca_system_score_gemma":0.0005193611,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01426389,"about_ca_topic_score_gemma":0.0131723,"domain_scores_codex":[0.9998254,0.00003737078,0.00000610421,0.00006327957,0.00003313149,0.00003475456],"domain_scores_gemma":[0.9995738,0.0002297427,0.00005322844,0.00003058235,0.00009094783,0.0000216691],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006173705,0.0000785158,0.0008140075,0.00002092841,0.00002387801,0.00005467268,0.00003208177,0.9331821,0.00115541,0.001408995,0.0009770475,0.06219064],"study_design_scores_gemma":[0.000001132762,0.000006775555,0.00007019284,8.008831e-7,0.000002559558,0.000002209475,0.000001314252,0.9991041,0.0001273288,0.0006379028,0.00004446462,0.000001195563],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2591531,0.001226539,0.727926,0.0008175254,0.0002091828,0.0001024174,0.0001981291,0.002717091,0.007650007],"genre_scores_gemma":[0.9784951,0.0002409567,0.01841485,0.0001535816,0.00006696023,0.00004066157,0.0001501062,0.0000351482,0.002402678],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01426389,"threshold_uncertainty_score":0.02836174,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0115148762542675,"score_gpt":0.1911490220515019,"score_spread":0.1796341457972344,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}