{"id":"W3090086577","doi":"10.1109/icrito48877.2020.9197882","title":"Maximum Power Tracking by Neural Network","year":2020,"lang":"en","type":"article","venue":"","topic":"Photovoltaic System Optimization Techniques","field":"Energy","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Maximum power point tracking; Photovoltaic system; Maximum power principle; Computer science; Renewable energy; Artificial neural network; Electricity generation; Controller (irrigation); MATLAB; Power (physics); Control theory (sociology); Voltage; Electronic engineering; Engineering; Electrical engineering; Artificial intelligence; Inverter; Control (management); Physics","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":[0.0004111692,0.0005267936,0.0005829767,0.0003939275,0.0003407147,0.0007666251,0.0006583752,0.001008218,0.002512098],"category_scores_gemma":[0.001157867,0.0003854974,0.0004667891,0.0006060919,0.0002671701,0.0007680722,0.0003701884,0.0007207886,0.0005249265],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006037405,"about_ca_system_score_gemma":0.0003963692,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005604368,"about_ca_topic_score_gemma":0.005184199,"domain_scores_codex":[0.9997249,0.00006040711,0.00001960634,0.00007707377,0.00008286776,0.00003513329],"domain_scores_gemma":[0.9996648,0.0001719346,0.00004372137,0.00001813143,0.00009365204,0.000007791318],"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":[0.00008496059,0.00003950782,0.0003827855,0.00009474434,0.00003391031,0.00004843019,0.00002547421,0.9074058,0.003136024,0.001244363,0.0006154014,0.08688852],"study_design_scores_gemma":[0.000002750657,0.000014664,0.0000739157,0.000005255675,0.000003597651,0.000007190277,0.000001704937,0.9987232,0.0005644707,0.0003742,0.0002265626,0.000002405975],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03214852,0.00125979,0.9530866,0.0002524191,0.0001026887,0.00006277172,0.00009179931,0.001492297,0.01150308],"genre_scores_gemma":[0.9101901,0.0007545952,0.07868233,0.0001108812,0.00005317725,0.0001768325,0.0001816351,0.00006006964,0.009790309],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005604368,"threshold_uncertainty_score":0.01114345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01815183167378486,"score_gpt":0.2317215148273405,"score_spread":0.2135696831535557,"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."}}