{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002164521,0.0008334459,0.0008001941,0.001084533,0.0004229183,0.001396358,0.001355857,0.001239043,0.003311277],"category_scores_gemma":[0.00721213,0.0005278297,0.001201199,0.001853399,0.0004413974,0.001514222,0.0004418637,0.001820734,0.0008774478],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001694558,"about_ca_system_score_gemma":0.0006971429,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03051893,"about_ca_topic_score_gemma":0.02335255,"domain_scores_codex":[0.9992353,0.0003314967,0.00005170179,0.0001402452,0.0001458595,0.00009555212],"domain_scores_gemma":[0.9977124,0.001605536,0.0002323572,0.0001082293,0.0002953875,0.00004596439],"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":[0.00003234711,0.00002513486,0.0004894235,0.00001550826,0.0000321131,0.00004140041,0.00003356149,0.9775569,0.0002560679,0.01252356,0.0003356412,0.008658307],"study_design_scores_gemma":[0.000002543616,0.000004967972,0.00008481928,0.000001675638,0.000003692588,0.000005348558,0.000003929948,0.997474,0.00005181448,0.002195219,0.0001685448,0.000003457283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1037146,0.0006316811,0.8870709,0.0008059231,0.0001224906,0.0000963518,0.001058374,0.001257417,0.00524222],"genre_scores_gemma":[0.8988596,0.0006840839,0.08986222,0.0001348215,0.00007757776,0.0001763581,0.001191122,0.0001396111,0.008874694],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03051893,"threshold_uncertainty_score":0.06068254,"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."}}