{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006617614,0.0004504203,0.0004273702,0.000282683,0.0005968909,0.0002600374,0.0004494422,0.0003066945,0.000005529483],"category_scores_gemma":[0.0004274197,0.0004123491,0.0001013727,0.00008226682,0.0002153318,0.0002071245,0.00009962967,0.0008173725,9.66707e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001855419,"about_ca_system_score_gemma":0.0001402552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000177168,"about_ca_topic_score_gemma":0.0001254611,"domain_scores_codex":[0.9980084,0.00001149039,0.0003329022,0.0006770643,0.0003691182,0.0006010061],"domain_scores_gemma":[0.9983987,0.0009759585,0.00009719153,0.000285327,0.0001260461,0.0001167424],"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":[0.00001535078,0.000003585159,0.000450566,0.0001906793,0.00000883061,0.00001775694,0.0003356545,0.3555642,0.001423851,0.00005716547,0.000003386154,0.641929],"study_design_scores_gemma":[0.0004440788,0.00009059061,0.00002632207,0.001549416,0.0000125916,0.0000222141,1.488687e-7,0.9867271,0.006618962,0.001294698,0.00273235,0.000481498],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003756833,0.0009575432,0.990563,0.0000280783,0.0007080535,0.0002701886,0.000006890896,0.0002079691,0.003501387],"genre_scores_gemma":[0.9319063,0.00004114573,0.06717189,0.00007253416,0.0004450357,0.00001177865,0.0000117455,0.00008819543,0.0002513274],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9281495,"threshold_uncertainty_score":0.9998328,"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."}}