{"id":"W4281482072","doi":"10.1016/j.apenergy.2022.120187","title":"Economic model predictive control of integrated energy systems: A multi-time-scale framework","year":2022,"lang":"en","type":"article","venue":"Applied Energy","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":60,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"National Key Research and Development Program of China; China Scholarship Council; Ministry of Education of the People's Republic of China; National Natural Science Foundation of China; Nanyang Technological University; Ministry of Education - Singapore","keywords":"Model predictive control; Scale (ratio); Control (management); Computer science; Engineering; Artificial intelligence; Geography","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001014545,0.0002519347,0.0004953241,0.0001487435,0.00008966767,0.0000156372,0.0002612355,0.0001300928,0.00005296904],"category_scores_gemma":[0.000005745486,0.0002861355,0.00007673344,0.0001761783,0.00003344391,0.00008163436,0.0000562179,0.0001717117,0.000005935433],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005214161,"about_ca_system_score_gemma":0.00005667845,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002428292,"about_ca_topic_score_gemma":0.00002095161,"domain_scores_codex":[0.998684,0.00005164474,0.0005053295,0.0002975251,0.0001657292,0.0002957034],"domain_scores_gemma":[0.9992591,0.00009431926,0.0001557109,0.0003726627,0.00003760967,0.0000806165],"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":[0.00008497778,0.00003327003,0.000006442833,0.00001467665,0.0001476856,8.204864e-7,0.0001049025,0.9644868,0.005282104,0.02898235,0.000175644,0.0006802765],"study_design_scores_gemma":[0.001131324,0.00004001239,0.000001687059,0.00001453896,0.00003517219,0.000002954135,0.0001973678,0.9945987,0.0008577093,0.000723674,0.002155039,0.0002418094],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0007757989,0.0005728466,0.9937306,0.000005307048,0.0003168223,0.0001970267,0.0002924771,0.0004197294,0.003689408],"genre_scores_gemma":[0.9930139,0.00003002458,0.005017561,0.00004421548,0.00008328107,0.001282031,0.000113933,0.0001007125,0.0003143332],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9922381,"threshold_uncertainty_score":0.9999591,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003607604384445226,"score_gpt":0.1745101585368291,"score_spread":0.1709025541523839,"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."}}