{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003899767,0.0001083925,0.0005644336,0.0001715929,0.00001392323,0.000008566102,0.00009719549,0.00003398144,0.0005546381],"category_scores_gemma":[0.0001177826,0.0001148717,0.0002749554,0.0007955353,0.000007784439,0.00007814863,0.00002257972,0.00008431891,0.000009502338],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009798283,"about_ca_system_score_gemma":0.00003081355,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002831504,"about_ca_topic_score_gemma":0.0001606619,"domain_scores_codex":[0.9990677,0.00003788338,0.0005164609,0.0001574001,0.00004166298,0.0001788832],"domain_scores_gemma":[0.9995062,0.0001388474,0.00009619251,0.0001990418,0.00002224047,0.00003746474],"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":[0.00001041024,0.0001108853,0.1893503,0.02696553,0.003677531,0.0001169136,0.0002093487,0.5224988,0.0008557008,0.002688003,0.00220575,0.2513108],"study_design_scores_gemma":[0.0002280995,0.00001072654,0.006090792,0.004322406,0.001214539,0.00002239997,0.00002247064,0.9685085,0.003800884,0.0001279648,0.0151659,0.0004852492],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4766974,0.3947361,0.003602525,0.00009461511,0.0001995236,0.0001419381,0.00001927661,0.00008513773,0.1244235],"genre_scores_gemma":[0.9174554,0.0812998,0.0009851099,0.0000652974,0.00004101091,0.000006736893,0.00002925705,0.00001401288,0.0001034106],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4460098,"threshold_uncertainty_score":0.6072898,"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."}}