{"id":"W1984016160","doi":"10.1109/ifsa-nafips.2013.6608579","title":"Predicting solar power output using complex fuzzy logic","year":2013,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Photovoltaic system; Computer science; Renewable energy; Fuzzy logic; Adaptive neuro fuzzy inference system; Electricity generation; Power (physics); Solar power; Grid-connected photovoltaic power system; Maximum power point tracking; Electric power system; Grid; Function (biology); Control engineering; Artificial intelligence; Fuzzy control system; Engineering; Electrical engineering; Inverter; Mathematics","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00007196466,0.0001551366,0.0001416955,0.0000606046,0.00009208294,0.00007484762,0.0001155647,0.00007420943,0.001006979],"category_scores_gemma":[0.00001706132,0.0001373776,0.00005308251,0.00010951,0.00002010378,0.0002288243,0.00004422031,0.0001407111,0.0002023849],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003440918,"about_ca_system_score_gemma":0.000005751504,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001521006,"about_ca_topic_score_gemma":0.000009866254,"domain_scores_codex":[0.999183,0.00001111337,0.0002069522,0.0001354864,0.000113052,0.0003504102],"domain_scores_gemma":[0.9996619,0.00003254039,0.0000216974,0.0001573351,0.0000345166,0.00009202342],"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":[0.00000513602,0.00007825553,0.06924087,0.0002127035,0.0002885598,0.00003549132,0.00227753,0.7554727,0.1284158,0.008628716,0.01541844,0.01992573],"study_design_scores_gemma":[0.0002554615,0.00003330045,0.007487189,0.00005484454,0.00001385614,0.00003824752,0.0002704469,0.9831288,0.002007365,0.0007903374,0.005490374,0.0004297184],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7369757,0.0001086495,0.01506257,0.00002530079,0.0005083593,0.00009235948,0.000002356653,0.0007807979,0.2464439],"genre_scores_gemma":[0.9877509,0.000003187419,0.01146941,0.0001434499,0.0001377178,0.000004582086,0.000005945828,0.0000379695,0.0004468618],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2507752,"threshold_uncertainty_score":0.9999062,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0359102840150187,"score_gpt":0.2250729077726183,"score_spread":0.1891626237575997,"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."}}