{"id":"W4366977989","doi":"10.1117/12.2657701","title":"Efficient community electricity load forecasting with transformer and federated learning","year":2023,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Electricity; Transformer; Deep learning; Data modeling; Artificial intelligence; Computation; Process (computing); Machine learning; Big data; Federated learning; Data mining; Database; Engineering","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":[0.0003341898,0.0001418175,0.0001332944,0.00007862632,0.0004529422,0.00005993922,0.00005092209,0.00005092928,0.00001936693],"category_scores_gemma":[0.0000331831,0.0001149532,0.00002004118,0.0005571939,0.00002495043,0.00004076422,0.00001160507,0.0004670383,0.00001130051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003920857,"about_ca_system_score_gemma":0.00001524556,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001663019,"about_ca_topic_score_gemma":0.0003447005,"domain_scores_codex":[0.9992645,0.00004101188,0.0001302148,0.00009316597,0.000133092,0.0003380565],"domain_scores_gemma":[0.9996569,0.0001613254,0.00001333554,0.00006293719,0.00003613954,0.00006939757],"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":[0.00002055907,0.00001453066,0.002481282,0.0001069341,0.00005255526,0.00001580234,0.002187902,0.9561027,0.005631416,0.0001280906,0.0001056965,0.03315255],"study_design_scores_gemma":[0.0003431526,0.00009561031,0.0009874452,0.00005721004,0.00000964251,0.00003414777,0.0004835142,0.9904078,0.006716684,0.00001413171,0.0006523957,0.0001982355],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9477196,0.00005509998,0.007655419,0.000013288,0.00004242788,0.00006256335,8.905632e-7,0.001060735,0.04338998],"genre_scores_gemma":[0.9993012,0.00001701052,0.0002475894,0.0000142707,0.00001973968,0.000006866273,0.00001005576,0.00003223444,0.0003510394],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0515816,"threshold_uncertainty_score":0.4687655,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01913129237438256,"score_gpt":0.2034425358914129,"score_spread":0.1843112435170303,"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."}}