{"id":"W3183491169","doi":"10.1145/3468784.3470656","title":"Building Energy Consumption Forecasting: A Comparison of Gradient Boosting Models","year":2021,"lang":"en","type":"article","venue":"","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Gradient boosting; Computer science; Boosting (machine learning); Machine learning; Energy consumption; Artificial intelligence; Building management system; Efficient energy use; Sustainability; Context (archaeology); Work (physics); Random forest; Engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.00004973307,0.00008855183,0.0001499417,0.00005560499,0.00004429011,0.00001528387,0.00005339084,0.00005728014,0.00004498193],"category_scores_gemma":[0.00001154965,0.0000960609,0.00004241747,0.0001412416,0.00001252408,0.0001151643,0.00002733519,0.00005785287,2.339088e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002933341,"about_ca_system_score_gemma":0.000009834325,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000166142,"about_ca_topic_score_gemma":0.0000241095,"domain_scores_codex":[0.9993947,0.00001176147,0.0002404888,0.0001118496,0.00009855962,0.0001426935],"domain_scores_gemma":[0.9997114,0.0000429482,0.00003888916,0.0001169258,0.00005406552,0.00003578877],"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":[0.000001849339,0.00001696701,0.0003669096,0.00003442188,0.00002033447,0.000001449996,0.00006628763,0.9178582,0.003749455,0.06866016,0.0001239248,0.009100035],"study_design_scores_gemma":[0.0001111547,0.000007783749,0.00001646244,0.00004971097,0.00001105769,0.000008729289,0.00002782299,0.9106901,0.08722512,0.001515193,0.0002414047,0.00009545899],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2063491,0.0005673912,0.7876098,0.000006681389,0.0001676873,0.00001546641,0.000001049127,0.0001801677,0.005102744],"genre_scores_gemma":[0.9416595,0.0000492389,0.05814818,0.00001409191,0.00002352794,0.000005591323,0.00001428383,0.00001722452,0.00006837198],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7353104,"threshold_uncertainty_score":0.391725,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05679535891102353,"score_gpt":0.2500825588592854,"score_spread":0.1932871999482619,"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."}}