{"id":"W2149384180","doi":"10.5539/cis.v8n1p1","title":"Short-Term Wind Power Forecasting Model based on ICA-BP Neural Network","year":2014,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Music and Audio Processing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Guangdong Province","keywords":"Artificial neural network; Computer science; Wind power forecasting; Wind power; Imperialist competitive algorithm; Power (physics); Term (time); Power grid; Artificial intelligence; Grid; Electric power system; Engineering; Electrical engineering; Mathematics; Metaheuristic","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0007976754,0.0001271606,0.0001155671,0.0001654707,0.0005714665,0.001057284,0.000718915,0.00003157298,0.00000273863],"category_scores_gemma":[0.00002639838,0.0001039061,0.00002808622,0.0005849451,0.0001628464,0.007118471,0.0002974347,0.0001113876,0.00001023015],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000200833,"about_ca_system_score_gemma":0.00007490135,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":3.383817e-7,"about_ca_topic_score_gemma":5.20816e-8,"domain_scores_codex":[0.9986644,0.00001679182,0.0002683327,0.0002481538,0.0004537776,0.0003485908],"domain_scores_gemma":[0.9992334,0.00006364937,0.00009903797,0.0003185911,0.0001405711,0.000144797],"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":[0.000002660861,0.000006813662,0.0005316887,0.00001222368,6.067566e-7,3.069889e-7,0.000681982,0.5180225,0.00001318535,0.009862979,0.0008504991,0.4700146],"study_design_scores_gemma":[0.0001371606,0.00006471504,0.007034927,0.0000413143,8.736741e-7,0.000008341309,0.000002063908,0.9912048,0.00004694149,0.0003767428,0.000940496,0.0001416227],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04743789,0.000004391331,0.9415078,0.0004054243,0.0003943882,0.00007428879,3.850149e-7,0.00008830259,0.01008709],"genre_scores_gemma":[0.9164097,6.939778e-7,0.0744027,0.00908998,0.00008612694,0.000001616314,0.000001293533,0.000002232641,0.000005622708],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8689718,"threshold_uncertainty_score":0.9999797,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02557376340524405,"score_gpt":0.2398219188010091,"score_spread":0.214248155395765,"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."}}