{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007820574,0.0006585669,0.0009392264,0.000481565,0.0004178976,0.0007881299,0.001714446,0.0008052744,0.001371646],"category_scores_gemma":[0.002080443,0.0002866244,0.0005553778,0.0008684187,0.0003944812,0.002322725,0.001390263,0.000919877,0.0004284634],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008275499,"about_ca_system_score_gemma":0.001533406,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01117194,"about_ca_topic_score_gemma":0.01125975,"domain_scores_codex":[0.9995354,0.0001053577,0.00002274855,0.0001388731,0.0001011894,0.00009643877],"domain_scores_gemma":[0.9994623,0.0001650963,0.00005303089,0.0001483983,0.0001214331,0.00004980993],"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":[0.0003198806,0.0002863327,0.003565622,0.00004957794,0.00005581325,0.0001362269,0.00008478134,0.7385768,0.002443525,0.006743912,0.005022467,0.2427151],"study_design_scores_gemma":[0.000006727935,0.00000917625,0.00008197983,0.000001003474,0.000002747341,0.000008691745,0.000008088804,0.997287,0.0003746203,0.002023698,0.000194225,0.000002103864],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08388194,0.0002643738,0.9071199,0.0005496652,0.00008534516,0.00006747741,0.0002414178,0.004192621,0.003597167],"genre_scores_gemma":[0.937952,0.00008098492,0.05926877,0.0001207649,0.00002946714,0.00004041258,0.0004077567,0.00004995211,0.002049857],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01117194,"threshold_uncertainty_score":0.02221382,"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."}}