{"id":"W3043435009","doi":"10.3390/electronics9071150","title":"Machine Learning Based PEVs Load Extraction and Analysis","year":2020,"lang":"en","type":"article","venue":"Electronics","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Artificial intelligence; Artificial neural network; Load profile; Energy (signal processing); Plug-in; Machine learning; Simulation; Engineering; Electricity; Mathematics; Statistics","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":[0.0001357852,0.0006748488,0.0004184178,0.001289326,0.0002119757,0.0004920923,0.0004776523,0.0004330713,0.002279096],"category_scores_gemma":[0.0006150157,0.0001670504,0.0004957909,0.0009429879,0.0001153196,0.0005429462,0.000273289,0.0003684683,0.001110746],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004865665,"about_ca_system_score_gemma":0.0003134042,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01203702,"about_ca_topic_score_gemma":0.008993517,"domain_scores_codex":[0.9998535,0.00001499844,0.000008891408,0.00003673004,0.00006460126,0.00002122887],"domain_scores_gemma":[0.9998447,0.00003816453,0.00001828837,0.00001788193,0.00007478362,0.00000623294],"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":[0.0001343244,0.00008639807,0.01022703,0.00009593867,0.00004719455,0.0002209846,0.0000443252,0.7962462,0.008560007,0.0009933537,0.002378538,0.1809657],"study_design_scores_gemma":[0.000001429798,0.000009693953,0.002461304,0.000002389226,0.000003315458,0.0000159089,0.00000940445,0.9954537,0.001394371,0.0002671691,0.0003768351,0.000004517894],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4550985,0.0003573659,0.524294,0.0002413271,0.0001107257,0.0001491829,0.00193583,0.004886798,0.01292637],"genre_scores_gemma":[0.9745976,0.0001076752,0.01991121,0.00002104838,0.00001710004,0.00004090563,0.001328041,0.00006762076,0.003908841],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01203702,"threshold_uncertainty_score":0.02393389,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004081755142517517,"score_gpt":0.1934009450718803,"score_spread":0.1893191899293628,"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."}}