{"id":"W2341910059","doi":"10.1109/tpwrs.2015.2438322","title":"A Novel Wavelet-Based Ensemble Method for Short-Term Load Forecasting with Hybrid Neural Networks and Feature Selection","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Power Systems","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":226,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Taiyuan University of Technology; University of Saskatchewan","keywords":"Extreme learning machine; Artificial neural network; Computer science; Feature selection; Ensemble learning; Artificial intelligence; Ensemble forecasting; Machine learning; Feature (linguistics); Wavelet; Wavelet transform; Term (time); Data mining","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.0007279328,0.0007161229,0.0009437482,0.0008948752,0.0003810775,0.000535302,0.001020968,0.0006391332,0.001137179],"category_scores_gemma":[0.001407222,0.0003147859,0.0008089134,0.001250752,0.0001573342,0.001447777,0.0006012964,0.0009262387,0.0004300289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002520065,"about_ca_system_score_gemma":0.0004274434,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002660479,"about_ca_topic_score_gemma":0.002531669,"domain_scores_codex":[0.9996131,0.00007738729,0.00002650591,0.00008790203,0.0001624663,0.00003274353],"domain_scores_gemma":[0.9996297,0.0001363015,0.00003778101,0.00004024822,0.0001397415,0.00001611512],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009937455,0.00008391592,0.001838143,0.00009049105,0.0001652482,0.00009740731,0.00006473521,0.4148458,0.008694746,0.00571366,0.003222257,0.5650842],"study_design_scores_gemma":[0.000002080275,0.000008049449,0.0001377142,0.000001957765,0.000007375552,0.00001239387,0.00000223099,0.998507,0.0004568361,0.0004956924,0.0003647892,0.000003921615],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006163071,0.0001887538,0.9928451,0.00003684449,0.00003914113,0.00001232143,0.00003966423,0.000227777,0.0004472955],"genre_scores_gemma":[0.408889,0.0008267438,0.5852708,0.0001085794,0.0002437037,0.0002251077,0.0006955304,0.0001597754,0.003580801],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002660479,"threshold_uncertainty_score":0.005289912,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02736188120161452,"score_gpt":0.2353228971134645,"score_spread":0.20796101591185,"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."}}