{"id":"W4412921088","doi":"10.1007/978-3-031-94862-6_6","title":"Forecasting Energy Prices Using Machine Learning Algorithms: A Comparative Analysis","year":2025,"lang":"en","type":"book-chapter","venue":"International series in management science/operations research/International series in operations research & management science","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Computer science; Machine learning; Energy (signal processing); Artificial intelligence; Algorithm; Mathematics; Statistics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","bibliometrics","sts","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":["sts"],"category_scores_codex":[0.009439671,0.0007145968,0.0006769236,0.02120758,0.003534463,0.004043658,0.005605642,0.0002006489,0.001154138],"category_scores_gemma":[0.0005177181,0.0007703647,0.0002065892,0.01013844,0.004601003,0.005819792,0.004182639,0.00189101,0.00004568005],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005068508,"about_ca_system_score_gemma":0.0005035127,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002050158,"about_ca_topic_score_gemma":0.02021058,"domain_scores_codex":[0.9871751,0.0002383769,0.00161036,0.002085014,0.007115664,0.001775453],"domain_scores_gemma":[0.9959341,0.0002761407,0.00009701491,0.0009971227,0.002395107,0.000300495],"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":[0.00004183367,0.00007469292,0.0001669823,0.00005471203,0.0003949042,0.0001317768,0.0006289336,0.5916072,0.0001574131,0.4043435,0.0001341674,0.002263947],"study_design_scores_gemma":[0.0004313039,0.00007408392,0.0002310101,0.0007043893,0.00005003072,0.00001573184,0.002238264,0.9109333,0.0003667486,0.003031482,0.08127736,0.0006463153],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.003726583,0.0004025978,0.009419581,0.001275918,0.002290023,0.001555295,0.0001934921,0.0002200906,0.9809164],"genre_scores_gemma":[0.4412877,0.009097593,0.05009006,0.00009071863,0.000455906,0.001128413,0.0009513324,0.0001123958,0.4967859],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.4841305,"threshold_uncertainty_score":0.9997745,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08620425709324156,"score_gpt":0.3872217502087985,"score_spread":0.3010174931155569,"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."}}