{"id":"W2121690610","doi":"10.1109/pmaps.2010.5528949","title":"Electricity market clearing price forecasting in a deregulated electricity market","year":2010,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Electricity market; Market clearing; Bidding; Electricity; Clearing; Electricity price forecasting; Artificial neural network; Computer science; Market price; Economics; Microeconomics; Artificial intelligence; Engineering; Finance","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0004933665,0.0002623711,0.0004835081,0.0002944337,0.0001718908,0.0006754436,0.000457409,0.0005176517,0.0005420746],"category_scores_gemma":[0.001633675,0.0002228033,0.000161709,0.0003817376,0.000203576,0.001068118,0.0001391779,0.0004667475,0.00008073303],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005723458,"about_ca_system_score_gemma":0.0003527458,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0081941,"about_ca_topic_score_gemma":0.00780288,"domain_scores_codex":[0.9998034,0.00005689529,0.00001099267,0.00004023353,0.00006376657,0.00002483974],"domain_scores_gemma":[0.9996021,0.0002219561,0.00005850416,0.00002025731,0.0000809639,0.00001623451],"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":[0.0000999177,0.00003860324,0.002009306,0.00001567466,0.00001785019,0.0001967702,0.00001796354,0.9762054,0.002932337,0.002777643,0.0003206448,0.01536784],"study_design_scores_gemma":[0.000001303009,0.000003511937,0.000390821,2.639152e-7,7.138682e-7,0.000004326167,0.000001659273,0.9991311,0.0002421507,0.0001899456,0.00003292155,0.000001200019],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8480926,0.0002481473,0.1470342,0.0002377527,0.00004315445,0.00002293697,0.0002155254,0.0002593317,0.003846356],"genre_scores_gemma":[0.9907171,0.00006307056,0.008558647,0.000007651613,0.000008782309,0.000005457404,0.0000914003,0.000008400495,0.0005395254],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0081941,"threshold_uncertainty_score":0.01629281,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008672922106375774,"score_gpt":0.1906627987948022,"score_spread":0.1819898766884264,"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."}}