{"id":"W4214511582","doi":"10.1007/978-3-030-96592-1_6","title":"Characterization of Residential Electricity Customers via Deep Ensemble Learning","year":2022,"lang":"en","type":"book-chapter","venue":"IFIP advances in information and communication technology","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Electricity; Identification (biology); Computer science; Ensemble learning; Classifier (UML); Consumption (sociology); Stock (firearms); Machine learning; Household income; Environmental economics; Ensemble forecasting; Demand response; Artificial intelligence; Engineering; Economics","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":[0.0001564481,0.0001518069,0.0002204559,0.001075012,0.00008887042,0.00001397359,0.0003209511,0.0002007421,0.000105029],"category_scores_gemma":[0.00002096236,0.0001951898,0.00002592821,0.0002208321,0.00008625616,0.000797814,0.0002059719,0.0005540855,0.00001200504],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001460336,"about_ca_system_score_gemma":0.000009964299,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006350117,"about_ca_topic_score_gemma":0.00003991679,"domain_scores_codex":[0.9990837,0.00001984599,0.0005442103,0.00008446584,0.0001477443,0.0001200296],"domain_scores_gemma":[0.99919,0.00003170976,0.0003157523,0.0003917549,0.00005628521,0.00001452455],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000231151,0.00000938235,0.0001120095,0.0002385036,0.00004561894,5.932329e-7,0.0004158098,0.08945839,0.0006221104,0.4559631,0.00002749564,0.4530839],"study_design_scores_gemma":[0.0002626315,0.00004346646,0.00007539034,0.00005338574,0.00001558035,0.000005746836,0.0001161662,0.02238786,0.001209895,0.007519293,0.9680702,0.0002403539],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.006329763,0.01042226,0.08030301,0.0003005821,0.0006067996,0.0008590053,0.00001381631,0.001291545,0.8998732],"genre_scores_gemma":[0.8300236,0.1636352,0.001191286,0.00007015563,0.00002066231,0.0002113519,0.001119061,0.00005734084,0.003671367],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.9680427,"threshold_uncertainty_score":0.7959608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003404036464153771,"score_gpt":0.1926563442394243,"score_spread":0.1892523077752705,"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."}}