{"id":"W2132421074","doi":"10.1109/icps.2007.4292100","title":"PCA-based Least Squares Support Vector Machines in Week-Ahead Load Forecasting","year":2007,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Independent Electricity System Operator","keywords":"Support vector machine; Computer science; Principal component analysis; Artificial neural network; Feature extraction; Artificial intelligence; Least squares support vector machine; Electricity; Feature (linguistics); Data mining; Machine learning; Pattern recognition (psychology); Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.001132673,0.0004704556,0.0006867458,0.0005655478,0.0002984392,0.0005798554,0.0004941334,0.0006650546,0.0006725495],"category_scores_gemma":[0.002907072,0.0003631287,0.0003560939,0.001027633,0.0002615467,0.0007693066,0.0002960068,0.0009724698,0.000489157],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002634462,"about_ca_system_score_gemma":0.0004233239,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008948566,"about_ca_topic_score_gemma":0.005309079,"domain_scores_codex":[0.999395,0.000284757,0.00003124599,0.00007243352,0.000163603,0.00005299259],"domain_scores_gemma":[0.9988438,0.0007637434,0.00006996813,0.00007653858,0.0002174728,0.00002841854],"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":[0.00021834,0.0001007535,0.001889621,0.00005811059,0.0000483623,0.00007819806,0.00007157151,0.6829481,0.004098366,0.00175245,0.001935416,0.3068008],"study_design_scores_gemma":[0.000001805276,0.00001224254,0.000364267,0.000001076779,0.000002095489,0.000006272014,0.00000447224,0.9984687,0.0006117315,0.0003513409,0.0001710732,0.000005021891],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09951831,0.0009953949,0.8960291,0.0002804892,0.00009620268,0.000037241,0.0001149173,0.001708346,0.001220018],"genre_scores_gemma":[0.8611048,0.0006778353,0.1351796,0.00004713526,0.00007346035,0.00006319219,0.0002216397,0.00008145096,0.002550854],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008948566,"threshold_uncertainty_score":0.01779294,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02242372112217885,"score_gpt":0.2302660827961961,"score_spread":0.2078423616740173,"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."}}