{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004900064,0.0002474863,0.0002282086,0.0002041917,0.00005788929,0.00003968167,0.0001555756,0.0001135309,0.0004590897],"category_scores_gemma":[0.00007491174,0.0002343962,0.00008024255,0.0003525381,0.00002766763,0.0001477744,0.0000244383,0.0002436417,0.00005442724],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001329674,"about_ca_system_score_gemma":0.00004345343,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003707693,"about_ca_topic_score_gemma":0.003983228,"domain_scores_codex":[0.9985059,0.00001268576,0.0004173767,0.0002168199,0.0002354171,0.0006117736],"domain_scores_gemma":[0.999441,0.0001727989,0.0000333262,0.0001856383,0.00003878673,0.0001284666],"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":[0.000204522,0.0003312376,0.1626929,0.0007497675,0.00009633287,0.0008188162,0.002044467,0.4724573,0.01565481,0.002836626,0.00580138,0.3363118],"study_design_scores_gemma":[0.002322644,0.0002977972,0.04004879,0.0004061688,0.00002395514,0.00008409821,0.0002692024,0.8845118,0.03770518,0.0001730479,0.03278218,0.001375118],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.825345,0.0002231565,0.01669311,0.00006501227,0.0008813053,0.000126731,0.000009234459,0.0007253322,0.1559311],"genre_scores_gemma":[0.9961267,0.000002423022,0.002821159,0.00009632674,0.0002523713,0.000006965768,0.00002238349,0.0000588669,0.0006127746],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4120545,"threshold_uncertainty_score":0.95584,"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."}}