{"id":"W3045941597","doi":"10.1109/icc40277.2020.9148937","title":"Electrical Load Forecasting Using Edge Computing and Federated Learning","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":246,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Smart meter; Deep learning; Big data; Smart grid; Enhanced Data Rates for GSM Evolution; Edge computing; Federated learning; Machine learning; Artificial intelligence; Scheme (mathematics); Data modeling; Edge device; Variety (cybernetics); Information privacy; Data mining; Computer security; Database; Engineering; Cloud computing","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002391129,0.0004787743,0.0005422187,0.00011032,0.0002950705,0.000357772,0.0001518067,0.000376676,0.00002600894],"category_scores_gemma":[0.0001968378,0.0005148248,0.0001043935,0.0002580573,0.00003034985,0.00007707872,0.0005459983,0.001856235,0.000006224909],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002075562,"about_ca_system_score_gemma":0.0001032467,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001091549,"about_ca_topic_score_gemma":0.00001610267,"domain_scores_codex":[0.9981216,0.00005907872,0.000487165,0.0005232831,0.0002411527,0.000567746],"domain_scores_gemma":[0.9993098,0.0001760565,0.0001065349,0.0001177856,0.00008228202,0.0002075269],"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":[0.000008564125,0.000007031283,0.00147733,0.0004740266,0.0001513414,0.00006857963,0.0006826542,0.9450969,0.004374821,0.0001622286,0.0001987775,0.04729781],"study_design_scores_gemma":[0.0001981525,0.00002518352,0.0000586896,0.0003745372,0.00004259236,0.00007034586,0.00004545608,0.9962078,0.001443611,0.0001316727,0.0008474525,0.0005544577],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7514983,0.002177193,0.2106847,0.00003840025,0.001005312,0.0002248889,0.000003831494,0.002163263,0.03220408],"genre_scores_gemma":[0.9832343,0.00005365967,0.01579189,0.00004939049,0.0006398962,0.000002644538,0.00003311462,0.0001253537,0.00006973947],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.231736,"threshold_uncertainty_score":0.9997303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.039734403292077,"score_gpt":0.2449674601139583,"score_spread":0.2052330568218813,"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."}}