{"id":"W2318900402","doi":"10.1080/09613218.2016.1101949","title":"Model-based predictive control of office window shades","year":2015,"lang":"en","type":"article","venue":"Building Research & Information","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Model predictive control; Window (computing); Air conditioning; Simulation; Control (management); Kalman filter; Engineering; Temperature control; Computer science; Control engineering; Artificial intelligence; Mechanical engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002188777,0.0003903976,0.0005474646,0.0001305138,0.0002960677,0.0006513256,0.0004892111,0.000288692,0.001069311],"category_scores_gemma":[0.0004380328,0.0002172832,0.000289604,0.0001707342,0.000396165,0.0003246379,0.0003922175,0.0005091138,0.0001203166],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004972664,"about_ca_system_score_gemma":0.0007014544,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0235563,"about_ca_topic_score_gemma":0.01510572,"domain_scores_codex":[0.9999013,0.00001813026,0.000003220565,0.00002171556,0.00003631895,0.00001933554],"domain_scores_gemma":[0.9998721,0.00005136014,0.00002727144,0.00001138792,0.00002909532,0.000008772819],"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":[0.0000249935,0.00000961354,0.0001607388,0.00001060356,0.000003376089,0.00001179757,0.00001284464,0.9940342,0.001224088,0.000640734,0.0001323093,0.003734638],"study_design_scores_gemma":[0.000002829081,0.000007311526,0.00008418024,6.646978e-7,0.000001258981,0.000001100814,0.000001886738,0.9993945,0.0002484726,0.0001683427,0.00008815176,0.000001193377],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.275083,0.0002648504,0.710344,0.0001937757,0.00006915726,0.00004763296,0.0001571875,0.001414445,0.01242598],"genre_scores_gemma":[0.9945078,0.00004691042,0.004348764,0.000007450347,0.000004374815,0.00001511395,0.00002505891,0.00001137162,0.001033128],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0235563,"threshold_uncertainty_score":0.04683834,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04144509715342746,"score_gpt":0.2884988524493488,"score_spread":0.2470537552959214,"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."}}