{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009978182,0.0001595746,0.0002110626,0.00007727878,0.00005414753,0.00002463536,0.00009562949,0.00008922897,0.0001851428],"category_scores_gemma":[0.00001751195,0.0001610413,0.0001529948,0.0005957838,0.00001764106,0.000128233,0.00001951235,0.0002080172,0.000002152769],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002620523,"about_ca_system_score_gemma":0.00001841024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005503344,"about_ca_topic_score_gemma":0.000238165,"domain_scores_codex":[0.9989484,0.00002808403,0.0003081501,0.0001757586,0.0001820468,0.0003575777],"domain_scores_gemma":[0.9995976,0.00007565497,0.00001659274,0.0001398915,0.0001057435,0.00006452701],"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":[0.00002967677,0.00003480252,0.002012956,0.0001114218,0.0002162769,0.0001329875,0.0004040282,0.8920263,0.06531271,0.000691839,0.0006014673,0.03842555],"study_design_scores_gemma":[0.0003525731,0.00003490266,0.0006804477,0.00004414177,0.00004386284,0.0000696938,0.00003555528,0.9143877,0.08386666,0.0001289196,0.0001290083,0.0002265073],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6941388,0.00148761,0.2876006,0.00001718153,0.000730843,0.00005999897,0.00000224822,0.000214288,0.0157484],"genre_scores_gemma":[0.9990331,0.00005222046,0.0004487906,0.00002654021,0.0001796356,0.000004306691,0.00001155233,0.00003507009,0.0002088081],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3048942,"threshold_uncertainty_score":0.6567073,"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."}}