{"id":"W2185482623","doi":"10.1109/pedes.2006.344294","title":"Electricity Price Forecasting Using Artificial Neural Network","year":2006,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Electricity; Electricity market; Artificial neural network; Backpropagation; Levenberg–Marquardt algorithm; Computer science; Artificial intelligence; Operations research; Engineering; Electrical engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003558365,0.0002471228,0.0003359452,0.0004419494,0.0001611944,0.0004918501,0.0003750183,0.0005098644,0.001062106],"category_scores_gemma":[0.001960548,0.0001646542,0.0001924604,0.0007415119,0.0001142672,0.0007693758,0.0001571955,0.0004188902,0.0003045163],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004423898,"about_ca_system_score_gemma":0.0002670575,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01129043,"about_ca_topic_score_gemma":0.009589215,"domain_scores_codex":[0.9998237,0.00004712354,0.00001423724,0.00003068941,0.00006822484,0.00001612051],"domain_scores_gemma":[0.9996766,0.0001640519,0.00003532709,0.0000190034,0.00009697848,0.000008063553],"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":[0.000162537,0.00007975911,0.005572089,0.00005578065,0.00006695848,0.0001293849,0.00002282397,0.8514186,0.00341744,0.001779829,0.002207732,0.1350871],"study_design_scores_gemma":[0.000002313832,0.000005532521,0.0005045273,0.000001427528,0.000002269096,0.000004792447,0.000001696718,0.99874,0.0002669027,0.0003229723,0.0001452613,0.000002323582],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3998816,0.001394711,0.582565,0.0008632267,0.0002732296,0.00004438192,0.0005734535,0.001771704,0.01263257],"genre_scores_gemma":[0.9635029,0.0003543639,0.03275401,0.00003548729,0.00004013421,0.00002004875,0.0003227119,0.00001707144,0.002953358],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01129043,"threshold_uncertainty_score":0.02244943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02567547207036745,"score_gpt":0.2078120334257407,"score_spread":0.1821365613553733,"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."}}