{"id":"W2046820006","doi":"10.1109/epec.2013.6802948","title":"A hybrid genetic radial basis function network with fuzzy corrector for short term load forecasting","year":2013,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Radial basis function; Term (time); Electric power system; Artificial neural network; Genetic algorithm; Adaptive neuro fuzzy inference system; Fuzzy logic; Scheduling (production processes); Mathematical optimization; Radial basis function network; Artificial intelligence; Fuzzy control system; Power (physics); Machine learning; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008604467,0.0006279956,0.0008810069,0.0006689277,0.0003950273,0.0007237128,0.001221632,0.001064996,0.00107525],"category_scores_gemma":[0.001192349,0.0003060157,0.0005896827,0.000754625,0.0002556459,0.0008378664,0.0002963818,0.0005918127,0.000362554],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006166456,"about_ca_system_score_gemma":0.0006397735,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01666548,"about_ca_topic_score_gemma":0.01280184,"domain_scores_codex":[0.9996722,0.00008947859,0.00001798501,0.00006441626,0.0001237697,0.00003216919],"domain_scores_gemma":[0.9997177,0.00008385901,0.00003381302,0.00001899611,0.0001356304,0.00001000228],"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":[0.0002879497,0.0001339373,0.001295086,0.0001096539,0.0001143218,0.0001176711,0.00006186204,0.7388612,0.007213484,0.002698769,0.001546839,0.2475592],"study_design_scores_gemma":[0.00001465323,0.00003703962,0.0002245443,0.000004258303,0.0000120324,0.00001823962,0.000003277105,0.99823,0.000792786,0.0002501213,0.0004041281,0.000008999985],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.060233,0.0007785279,0.9335012,0.0001815975,0.000115565,0.00009176762,0.0001023933,0.001362332,0.003633568],"genre_scores_gemma":[0.7238215,0.0004838113,0.2702182,0.0001096971,0.00005885703,0.000193925,0.0002270393,0.00006614091,0.004820885],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01666548,"threshold_uncertainty_score":0.03313696,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01276868181563274,"score_gpt":0.1805207633037038,"score_spread":0.1677520814880711,"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."}}