{"id":"W2048178471","doi":"10.1115/dscc2013-3897","title":"A Model-Based Predictive Control Approach for a Building Cooling System With Ice Storage","year":2013,"lang":"en","type":"article","venue":"","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Model predictive control; Thermal energy storage; Chiller; Computer science; Duty cycle; Constraint (computer-aided design); Electricity; Water cooling; Control theory (sociology); Engineering; Control (management); Voltage; Mechanical 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003406417,0.000734938,0.0007714329,0.000230874,0.0005319843,0.0008541462,0.0008357861,0.0006647747,0.002210716],"category_scores_gemma":[0.0005418285,0.0003379151,0.0004800993,0.0003215162,0.0004795639,0.000465863,0.0004338761,0.0009871277,0.0002567224],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006268859,"about_ca_system_score_gemma":0.00111586,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0129506,"about_ca_topic_score_gemma":0.01001171,"domain_scores_codex":[0.9998742,0.00002692156,0.000004598302,0.00002679112,0.00005038444,0.00001693274],"domain_scores_gemma":[0.9998565,0.00006832604,0.0000207649,0.000008721014,0.00003698131,0.000008768173],"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":[0.00001600888,0.00001267994,0.00006441485,0.00003342075,0.000008608392,0.00002555688,0.00001518759,0.9861731,0.0008292265,0.002337526,0.0002443964,0.01023987],"study_design_scores_gemma":[0.000003967734,0.00001306686,0.00002113484,0.000001913567,0.000002967507,0.000003320773,0.000001907778,0.9991484,0.0001941004,0.0003669813,0.0002406695,0.000001598298],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01289822,0.0003713443,0.9796669,0.0002157483,0.00007173426,0.00006037854,0.00004442535,0.0004028263,0.00626843],"genre_scores_gemma":[0.9166278,0.0005028895,0.07773431,0.00007686567,0.00007109041,0.0002377308,0.00008532833,0.00004711213,0.004616862],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0129506,"threshold_uncertainty_score":0.02575046,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006035370279811045,"score_gpt":0.1829601586472087,"score_spread":0.1769247883673977,"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."}}