{"id":"W4310881603","doi":"10.1145/3563357.3566155","title":"Unsupervised energy disaggregation using time series decomposition for commercial buildings","year":2022,"lang":"en","type":"article","venue":"","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Resources Canada; National Research Council Canada","keywords":"Computer science; Energy consumption; Decomposition; Energy (signal processing); Automation; Measure (data warehouse); Efficient energy use; Industrial engineering; Data mining; Real-time computing; Engineering","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":[0.00006355243,0.00009238898,0.0000910906,0.00007205274,0.0003269057,0.00002922629,0.00008343921,0.00003331041,0.0002526387],"category_scores_gemma":[0.000002158017,0.0001083378,0.00004604055,0.0001585926,0.000009891787,0.0002214577,0.00003325204,0.00004592953,4.177519e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001080138,"about_ca_system_score_gemma":0.000009239346,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003200213,"about_ca_topic_score_gemma":0.000008154221,"domain_scores_codex":[0.9995198,0.00001668448,0.000131152,0.0001040039,0.00009317729,0.0001351575],"domain_scores_gemma":[0.9998246,0.0000209144,0.00002126489,0.00008665492,0.00002066271,0.00002588761],"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":[0.00002669498,0.00001351131,0.00003106743,0.000008188771,0.0000164783,2.544891e-7,0.00004581717,0.9753392,0.009694915,0.01011792,0.001014745,0.003691224],"study_design_scores_gemma":[0.0002654796,0.00004578997,0.0000223661,0.000005305455,0.00001692474,0.000009882748,0.0000211139,0.9753061,0.01137788,0.0008707689,0.01189141,0.0001669903],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2235679,0.0000688607,0.7742719,0.0001215433,0.0004074351,0.0001127286,0.00002357611,0.0005100056,0.000916046],"genre_scores_gemma":[0.9635286,0.00001130943,0.03523998,0.0001715248,0.0001199661,0.0001100137,0.0003673472,0.00004658376,0.0004046391],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7399607,"threshold_uncertainty_score":0.4417888,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008051905791031058,"score_gpt":0.2112929430899038,"score_spread":0.2032410372988727,"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."}}