{"id":"W2964114610","doi":"10.26868/25222708.2017.351","title":"MPCPy: an Open-Source Software Platform for Model Predictive Control in Buildings","year":2017,"lang":"en","type":"article","venue":"Building Simulation Conference proceedings","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canada Excellence Research Chairs, Government of Canada; U.S. Department of Energy","keywords":"Model predictive control; Usability; Schedule; Computer science; Building management system; Control (management); Software; Grid; Demand response; PID controller; Architecture; Software architecture; Systems engineering; Quality (philosophy); Architectural engineering; Control engineering; Engineering; Electricity; Human–computer interaction; Operating system; Temperature control","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0004769822,0.0003533854,0.0004856032,0.0002119818,0.0005187416,0.001119296,0.001044013,0.000242981,0.000006481028],"category_scores_gemma":[0.0008138972,0.0003997113,0.00005202094,0.0001064308,0.00006114442,0.004866029,0.0001265607,0.0002323313,0.000002069292],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002727487,"about_ca_system_score_gemma":0.00006214038,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003380623,"about_ca_topic_score_gemma":0.00001781417,"domain_scores_codex":[0.9981169,0.000005308542,0.0005515896,0.0005617547,0.0002539914,0.0005104818],"domain_scores_gemma":[0.9984031,0.0001406758,0.0003589485,0.0003302762,0.0006137412,0.0001532125],"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":[0.0001775075,0.00002351694,0.003588564,0.00008617326,0.00002471668,2.852362e-7,0.0007684698,0.9770353,0.00404442,0.007370202,0.00002703343,0.006853811],"study_design_scores_gemma":[0.003396176,0.0001052987,0.0007443264,0.0002314653,0.00002913172,0.000001436317,0.0001386848,0.9829605,0.0003700798,0.01117404,0.0004097304,0.0004390871],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1331265,0.00002023881,0.8636568,0.00005802122,0.0001402826,0.001864285,0.00003885426,0.0005610402,0.0005339865],"genre_scores_gemma":[0.9338679,0.000005843769,0.06521135,0.00005493954,0.0001379455,0.0004948401,0.00001611067,0.0001116086,0.00009946609],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8007414,"threshold_uncertainty_score":0.9999176,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03562850275045829,"score_gpt":0.3011919641641357,"score_spread":0.2655634614136774,"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."}}