{"id":"W4396780668","doi":"10.1117/12.3012012","title":"Federated autoML learning for community building energy prediction","year":2024,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Okanagan University College; University of British Columbia","funders":"","keywords":"Computer science; Automation; Analytics; Data modeling; Energy management; Resource (disambiguation); Artificial intelligence; Deep learning; Energy modeling; Building management system; Machine learning; Energy (signal processing); Efficient energy use; Big data; Data science; Software engineering; Data mining; Engineering; Control (management)","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":[0.001059253,0.0009727863,0.001202666,0.001030003,0.0006711643,0.0008194451,0.001717283,0.0009611708,0.00290121],"category_scores_gemma":[0.002337994,0.000407014,0.0006601203,0.001002927,0.0004830702,0.001729062,0.001687031,0.001399317,0.001024887],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001212117,"about_ca_system_score_gemma":0.00162715,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01984008,"about_ca_topic_score_gemma":0.02781939,"domain_scores_codex":[0.9995071,0.00009795672,0.00002290111,0.0001498498,0.0001179808,0.0001042838],"domain_scores_gemma":[0.9993184,0.0001983818,0.00005440053,0.0001705864,0.000196559,0.00006169804],"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":[0.0002926552,0.0006035499,0.004931634,0.00004715015,0.00006747426,0.0001228379,0.00006231096,0.6846616,0.001880533,0.002720592,0.006455882,0.2981537],"study_design_scores_gemma":[0.00000510233,0.000008705593,0.000116856,0.000001374589,0.000002012724,0.000003656859,0.000006230252,0.9979963,0.0002985447,0.001398399,0.0001608864,0.000001968815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1395807,0.0005649718,0.8376777,0.0007096427,0.0001600762,0.0001613059,0.001012573,0.0147745,0.005358606],"genre_scores_gemma":[0.9135676,0.00007937574,0.08189811,0.0002025109,0.00004257739,0.0001235114,0.001305982,0.000119391,0.002660841],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01984008,"threshold_uncertainty_score":0.03944921,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01570874405056611,"score_gpt":0.2290043300986378,"score_spread":0.2132955860480717,"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."}}