{"id":"W4391451243","doi":"10.3390/forecast6010007","title":"Forecasting the Occurrence of Electricity Price Spikes: A Statistical-Economic Investigation Study","year":2024,"lang":"en","type":"article","venue":"Forecasting","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Interpretability; Computer science; Decision tree; Machine learning; Hyperparameter; Econometrics; Artificial intelligence; Electricity; Electricity price forecasting; Statistical model; Binary classification; Random forest; Electricity market; Data mining; Support vector machine; Economics; 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":[0.0007656684,0.0002499311,0.0002559401,0.0001625668,0.0001673314,0.0001329027,0.0002544195,0.00006221388,0.00005667483],"category_scores_gemma":[0.000294935,0.0001993249,0.00007056505,0.0005311812,0.0000796531,0.000263543,0.00006403036,0.0003879084,0.00001919184],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001232831,"about_ca_system_score_gemma":0.00008075048,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001065706,"about_ca_topic_score_gemma":0.00007269232,"domain_scores_codex":[0.9983888,0.00006222189,0.000595955,0.0003053729,0.000208855,0.0004388014],"domain_scores_gemma":[0.998572,0.0009879251,0.00009159921,0.0002225882,0.00004058623,0.00008533822],"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":[0.00004524835,0.0001183783,0.04084082,0.002340077,0.0005271103,0.0001787626,0.0235731,0.5932951,0.003252628,0.01010619,0.005679704,0.3200428],"study_design_scores_gemma":[0.0001332105,0.0001430524,0.0003965984,0.0002861722,0.00005527335,0.00004909267,0.0003753318,0.9952157,0.001582935,0.0007573875,0.0007694293,0.0002358877],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9681378,0.000697758,0.02359568,0.00001721057,0.001053875,0.0003755181,0.00009465115,0.0004356601,0.005591863],"genre_scores_gemma":[0.9981874,0.000009077829,0.001390986,0.000009996486,0.000260635,0.00004296286,0.00002729749,0.00004161276,0.00002998681],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4019205,"threshold_uncertainty_score":0.8128234,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03612643993758135,"score_gpt":0.2410028268789844,"score_spread":0.204876386941403,"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."}}