{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01076395,0.0005244136,0.0005416116,0.001219134,0.0005754338,0.001745349,0.0009117359,0.001131408,0.0008458227],"category_scores_gemma":[0.05314022,0.0002469473,0.000555206,0.001774505,0.001149146,0.001890955,0.0005943793,0.00111917,0.0001655512],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002194846,"about_ca_system_score_gemma":0.001590172,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02123976,"about_ca_topic_score_gemma":0.01604199,"domain_scores_codex":[0.9963301,0.001644715,0.0002363727,0.000352157,0.00124409,0.0001926256],"domain_scores_gemma":[0.9327081,0.05926638,0.002873663,0.002138255,0.002728537,0.0002851061],"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":[0.003204718,0.00330211,0.3296233,0.0003908323,0.0005251527,0.000905587,0.001075332,0.5066088,0.01540433,0.02547747,0.002812593,0.1106697],"study_design_scores_gemma":[0.0001071423,0.001114995,0.07856174,0.00002181883,0.00007915798,0.0001278285,0.0007938228,0.9049607,0.007245835,0.006138243,0.0007821227,0.00006661411],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9812541,0.0001161742,0.01469577,0.0006409701,0.00002555953,0.0001865306,0.0003305532,0.0001009449,0.002649369],"genre_scores_gemma":[0.9926907,0.00004875253,0.006659317,0.00004882447,0.00001635741,0.00005587321,0.0002298292,0.000008656729,0.0002416033],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02123976,"threshold_uncertainty_score":0.05692595,"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."}}