{"id":"W4317627131","doi":"10.1016/j.eneco.2023.106521","title":"Foreseeing the worst: Forecasting electricity DART spikes","year":2023,"lang":"en","type":"article","venue":"Energy Economics","topic":"Electric Power System Optimization","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University; Group for Research in Decision Analysis; HEC Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; HEC Montréal; Hydro-Québec","keywords":"Dart; Computer science; Spike (software development); Set (abstract data type); Predictive power; Electricity; Feature (linguistics); Operator (biology); Probabilistic forecasting; Econometrics; Machine learning; Artificial intelligence; Economics; Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.0009184058,0.0005011251,0.0005061234,0.0006141997,0.000312555,0.001542447,0.0004329085,0.001256597,0.0007954089],"category_scores_gemma":[0.005066684,0.000232355,0.000303252,0.0008049725,0.0003018127,0.001461823,0.000477668,0.001376298,0.0001882503],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007269356,"about_ca_system_score_gemma":0.0004407854,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01132078,"about_ca_topic_score_gemma":0.01162487,"domain_scores_codex":[0.9998161,0.00004045161,0.00001185588,0.00004978438,0.00004447747,0.00003740795],"domain_scores_gemma":[0.9989172,0.000576329,0.000159932,0.00005794226,0.000147156,0.0001414382],"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":[0.0006302109,0.0001898979,0.1098871,0.00005524589,0.0001537389,0.0005873096,0.00009946414,0.8456675,0.00178571,0.00611495,0.008992014,0.02583685],"study_design_scores_gemma":[0.000012611,0.00003901763,0.01385777,0.000007451285,0.00001188472,0.00003272221,0.0001234663,0.9805658,0.0005107507,0.004023266,0.0008030877,0.00001213365],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9809786,0.0004080489,0.009368818,0.002086987,0.0001858698,0.00002301501,0.001079583,0.0002122062,0.005656935],"genre_scores_gemma":[0.998493,0.00007962971,0.000761482,0.00004182818,0.00003709643,0.000003158473,0.0003013307,0.0000112732,0.0002711008],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01132078,"threshold_uncertainty_score":0.02250975,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01344774014926228,"score_gpt":0.1667998384117479,"score_spread":0.1533520982624857,"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."}}