{"id":"W3189259054","doi":"10.1109/access.2021.3098631","title":"Online Unsupervised Occupancy Anticipation System Applied to Residential Heat Load Management","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hydro-Québec; Université du Québec à Trois-Rivières","funders":"Natural Sciences and Engineering Research Council of Canada; Hydro-Québec; Université du Québec à Trois-Rivières","keywords":"Occupancy; Computer science; Energy consumption; Anticipation (artificial intelligence); Electricity; Consistency (knowledge bases); Energy management; Thermal comfort; Building management system; Monte Carlo method; Simulation; Energy (signal processing); Control (management); Artificial intelligence; Statistics; Engineering; Architectural engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00005228414,0.000112955,0.0001293463,0.00007696639,0.0000603807,0.0001117905,0.0001901613,0.00005630912,0.00003022226],"category_scores_gemma":[0.000002188764,0.0001261442,0.00002937531,0.0003891779,0.000004081276,0.0001572706,0.00005199456,0.00005872379,0.00001305966],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001144489,"about_ca_system_score_gemma":0.00001507079,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003323736,"about_ca_topic_score_gemma":0.00007689097,"domain_scores_codex":[0.9992329,0.00001177803,0.0001930554,0.000190773,0.0001972593,0.0001741766],"domain_scores_gemma":[0.9996052,0.000008105764,0.00001177219,0.0002626846,0.00005674507,0.00005548684],"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":[0.00001362192,0.0000310859,0.0001344955,0.0002092824,0.00004585113,0.00002471559,0.00004851736,0.992816,0.002046648,0.0009415722,0.001510351,0.0021778],"study_design_scores_gemma":[0.002488705,0.00003445894,0.02259852,0.000708912,0.0002606295,0.0000211877,0.000363384,0.7494723,0.2175298,0.0001777768,0.005108432,0.001235898],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7367087,0.0001045985,0.2547496,0.00004803792,0.001680775,0.0002001019,0.00001037347,0.0005515233,0.005946331],"genre_scores_gemma":[0.9970142,0.00004124807,0.002377197,0.0001095896,0.0001798684,0.00004707995,0.0000644483,0.00002789562,0.0001385354],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2603055,"threshold_uncertainty_score":0.5144011,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01874989468738275,"score_gpt":0.2617783210670608,"score_spread":0.2430284263796781,"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."}}