{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009128984,0.000238513,0.0002067632,0.00004647861,0.0001285985,0.00009145431,0.00009934239,0.00006055668,0.0002286525],"category_scores_gemma":[0.00001730537,0.0001981936,0.00007877556,0.0001249927,0.00002139266,0.0001814377,0.0000180938,0.0001154235,0.00002862458],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007945254,"about_ca_system_score_gemma":0.00002672565,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007132156,"about_ca_topic_score_gemma":0.0001445452,"domain_scores_codex":[0.998832,0.00000987255,0.0002452909,0.0002339154,0.0001488641,0.0005300146],"domain_scores_gemma":[0.9994707,0.0001155995,0.0000263477,0.0001761913,0.00007960702,0.0001315163],"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":[0.00009580766,0.00002321473,0.02137962,0.0001516846,0.0002290482,0.00001010183,0.0001458912,0.5560565,0.001291476,0.0001363365,0.03310145,0.3873789],"study_design_scores_gemma":[0.0008482474,0.0004493445,0.01009474,0.0001789939,0.000121526,0.0001247474,0.00003964577,0.9734056,0.002163978,0.0003609022,0.01143915,0.0007731189],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7869443,0.000267085,0.1838653,0.00001310059,0.001778165,0.0005392921,0.000006916677,0.0006119212,0.02597386],"genre_scores_gemma":[0.9811873,0.000009208611,0.01647959,0.0000668322,0.00134958,0.0002279972,0.00002571088,0.00008197597,0.0005717857],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4173491,"threshold_uncertainty_score":0.80821,"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."}}