{"id":"W1583891148","doi":"10.1109/nafips.2001.943625","title":"Fuzzy regression models to represent electricity market data in deregulated power industry","year":2002,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"University of Tokyo","keywords":"Electricity market; Fuzzy logic; Regression analysis; Econometrics; Electricity; Regression; Data modeling; Supply and demand; Computer science; Power demand; Power (physics); Electric power system; Electric power industry; Demand forecasting; Economics; Microeconomics; Engineering; Statistics; Mathematics; Operations management; Artificial intelligence; Electrical engineering; Machine learning; Power consumption","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":[0.0001820264,0.0001556562,0.0001549485,0.0001556314,0.00003038697,0.00002943473,0.000349209,0.0002307444,0.0008760373],"category_scores_gemma":[0.00004415512,0.0001354834,0.0000189899,0.0005282255,0.000006927023,0.0002881251,0.0001777973,0.0003984298,0.00002616165],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000497562,"about_ca_system_score_gemma":0.000004676265,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000102392,"about_ca_topic_score_gemma":0.00008081797,"domain_scores_codex":[0.998911,0.00002857525,0.0002403686,0.0002996031,0.0001690538,0.0003513772],"domain_scores_gemma":[0.9990612,0.00004561157,0.00001655197,0.0007360312,0.00001614873,0.0001244606],"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":[0.00003532167,0.0001294904,0.004074623,0.00004316343,0.00004510231,0.0001097418,0.000723369,0.5805544,0.003454239,0.0008316358,0.3749964,0.03500249],"study_design_scores_gemma":[0.0002279033,0.00001532032,0.0009789014,0.0001220695,0.000003556803,0.00001220793,0.00003131076,0.9916387,0.0027579,0.0003300297,0.003630999,0.0002511513],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.4740796,0.0004705125,0.002561936,0.0001802113,0.000224167,0.0001337896,0.00001139164,0.0004085881,0.5219298],"genre_scores_gemma":[0.9942603,0.00003743437,0.001741207,0.00009055078,0.00004275435,0.00000482765,0.00001750683,0.00002957176,0.003775842],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5201807,"threshold_uncertainty_score":0.9591994,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04804967602957257,"score_gpt":0.2454557681736094,"score_spread":0.1974060921440368,"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."}}