{"id":"W4391560351","doi":"10.1080/23744731.2024.2304539","title":"Energy consumption disaggregation in commercial buildings: a time series decomposition approach","year":2024,"lang":"en","type":"article","venue":"Science and Technology for the Built Environment","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; National Research Council Canada","funders":"National Research Council Canada","keywords":"Energy consumption; Energy flow; Computer science; Efficient energy use; Energy (signal processing); Energy accounting; Audit; Reliability engineering; Environmental science; Engineering; Accounting","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002186297,0.00007728372,0.00006252956,0.0002063421,0.0001724321,0.00004737838,0.0001393913,0.00007467308,0.000006539251],"category_scores_gemma":[0.000004775108,0.00006127377,0.00001221045,0.000292014,0.0004091626,0.0002244335,0.00004615276,0.00007025063,0.000001849363],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009663792,"about_ca_system_score_gemma":0.000008692643,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004453684,"about_ca_topic_score_gemma":0.000003015975,"domain_scores_codex":[0.999483,0.000003848179,0.00009473193,0.0001724844,0.00009699331,0.0001489729],"domain_scores_gemma":[0.9998257,0.00002244162,0.00001131491,0.0001207003,0.000005019869,0.0000147965],"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":[0.00001842526,0.00004565986,0.0005853698,0.00006955501,0.00003142073,0.000001526047,0.000204772,0.402773,0.04488715,0.1256274,0.0002641174,0.4254917],"study_design_scores_gemma":[0.000120453,0.0000397592,0.000368809,0.00003373464,0.00001585538,0.00002205001,0.00003485292,0.9664405,0.01944717,0.005689467,0.007666714,0.0001206679],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3669467,0.003480348,0.6264966,0.001824849,0.0002580221,0.0003031644,0.000008503505,0.0004669926,0.0002148746],"genre_scores_gemma":[0.9947886,0.0009256491,0.00402977,0.00001990638,0.00001814944,0.0001513978,0.000009894256,0.000009417175,0.00004718063],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6278419,"threshold_uncertainty_score":0.2498672,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006286018614924577,"score_gpt":0.2093406608114216,"score_spread":0.203054642196497,"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."}}