{"id":"W2034374099","doi":"10.1080/15325000802599353","title":"Day-ahead Price Forecasting in Ontario Electricity Market Using Variable-segmented Support Vector Machine-based Model","year":2009,"lang":"en","type":"article","venue":"Electric Power Components and Systems","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Independent Electricity System Operator","keywords":"Support vector machine; Autoregressive model; Artificial neural network; Volatility (finance); Autoregressive integrated moving average; Time series; Electricity market; Moving-average model; Computer science; Heuristic; Econometrics; Electricity; Engineering; Artificial intelligence; Machine learning; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002590286,0.0002841203,0.0003565889,0.0002438096,0.0002114424,0.0004850694,0.000482878,0.0002808007,0.0007148685],"category_scores_gemma":[0.000987489,0.0001257596,0.0002643633,0.0002877509,0.0001510605,0.0002669766,0.0001077334,0.0002333727,0.0001037762],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001031024,"about_ca_system_score_gemma":0.00101802,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2096557,"about_ca_topic_score_gemma":0.15188,"domain_scores_codex":[0.999876,0.00002812986,0.000006809849,0.00002192455,0.00004359037,0.00002344835],"domain_scores_gemma":[0.9997851,0.00008224963,0.00002804818,0.00001006398,0.00008439169,0.00001017273],"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":[0.0001415337,0.00003895311,0.004534997,0.00003445171,0.0000430178,0.0001080762,0.0000427717,0.968272,0.001757217,0.000979076,0.0007095604,0.02333841],"study_design_scores_gemma":[0.000001606261,0.000006548555,0.0007683579,5.644402e-7,0.000001690809,0.000001874656,0.000002567987,0.9990419,0.00007865603,0.00006081296,0.0000342574,0.000001208347],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.928189,0.0002286972,0.06777912,0.0002054839,0.00003692267,0.00002340731,0.0003006769,0.0002337179,0.003002875],"genre_scores_gemma":[0.9971408,0.00003099157,0.002089223,0.000003826019,0.000003354346,0.000005162395,0.00009496605,0.000002454958,0.0006292897],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7903444,"threshold_uncertainty_score":0.4168706,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02592799134333872,"score_gpt":0.2063056999490188,"score_spread":0.1803777086056801,"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."}}