{"id":"W3117702476","doi":"10.15353/rea.v13i1.1822","title":"Forecasting Price Spikes in Electricity Markets","year":2021,"lang":"en","type":"article","venue":"Review of Economic Analysis","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Electricity; Electricity price forecasting; Econometrics; Support vector machine; Economics; Electricity market; Commodity; Generalization; Artificial neural network; Electricity price; Pareto principle; Generalized Pareto distribution; Quantile; Sample (material); Computer science; Artificial intelligence; Statistics; Mathematics; Extreme value theory","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.0009716779,0.0004179566,0.0006346672,0.001198464,0.0001677855,0.000973592,0.0004819882,0.000671818,0.0004327874],"category_scores_gemma":[0.003877633,0.0001934547,0.0003145309,0.00147291,0.0001840911,0.001059809,0.0004320885,0.0007347731,0.0001992327],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004457537,"about_ca_system_score_gemma":0.000210385,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004642727,"about_ca_topic_score_gemma":0.00319596,"domain_scores_codex":[0.9996364,0.00006977361,0.00002916474,0.00008233132,0.0001297695,0.00005250709],"domain_scores_gemma":[0.999064,0.0004682918,0.0001997361,0.00005085017,0.000168739,0.00004837778],"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":[0.0004924276,0.000256665,0.1432108,0.0001578598,0.0001948723,0.0005082901,0.0001266503,0.6674002,0.003686991,0.004637497,0.009412749,0.169915],"study_design_scores_gemma":[0.000004718655,0.00001412958,0.01489002,0.000005724779,0.000006189614,0.00001897846,0.00002472118,0.9828969,0.0003996352,0.001349287,0.0003845367,0.000005183758],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9730049,0.001158617,0.02235917,0.0004495624,0.0001022861,0.00001725292,0.0009843011,0.000366679,0.00155722],"genre_scores_gemma":[0.9965305,0.0002597133,0.00213994,0.00001845874,0.0000567977,0.000004406863,0.0007912665,0.00001058836,0.0001884549],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004642727,"threshold_uncertainty_score":0.009231389,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01432513721178229,"score_gpt":0.2268219642484462,"score_spread":0.2124968270366639,"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."}}