{"id":"W2775854174","doi":"10.1007/978-3-319-71273-4_4","title":"Boosting Based Multiple Kernel Learning and Transfer Regression for Electricity Load Forecasting","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Boosting (machine learning); Smart grid; Electricity; Kernel (algebra); Machine learning; Scheduling (production processes); Ensemble learning; Multiple kernel learning; Probabilistic forecasting; Computation; Artificial intelligence; Demand response; Support vector machine; Gradient boosting; Mathematical optimization; Kernel method; Random forest; Engineering; Algorithm","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":[0.00141473,0.0004996257,0.00129874,0.0005497516,0.0003474324,0.0006397122,0.001411097,0.001017392,0.001977884],"category_scores_gemma":[0.003402773,0.0003843218,0.0008321096,0.00115638,0.000341004,0.001089071,0.000874899,0.001638018,0.001248091],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004735084,"about_ca_system_score_gemma":0.0004667264,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002398302,"about_ca_topic_score_gemma":0.001802594,"domain_scores_codex":[0.9995319,0.0001887506,0.00002653364,0.0000764595,0.0001285903,0.00004780978],"domain_scores_gemma":[0.9990036,0.0005024203,0.00004807394,0.0001328785,0.0002847337,0.00002827838],"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":[0.0002291199,0.0001610512,0.0006679513,0.0001357745,0.0001009895,0.00005835375,0.00006261268,0.4947284,0.006113622,0.011478,0.006964644,0.4792996],"study_design_scores_gemma":[0.000001681178,0.000008674715,0.0001068775,0.000001765186,0.000003757218,0.000004844025,0.000001275156,0.9978023,0.0003395638,0.001414369,0.0003124494,0.000002378736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01986842,0.001634212,0.9758794,0.0001405561,0.0001747458,0.00002113109,0.00004658824,0.0009028348,0.001332191],"genre_scores_gemma":[0.6847042,0.0013575,0.3014231,0.0001315461,0.0002688944,0.00009625516,0.0003863879,0.0002829175,0.01134935],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002398302,"threshold_uncertainty_score":0.007481873,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02242800543985167,"score_gpt":0.231501388960252,"score_spread":0.2090733835204003,"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."}}