{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001706513,0.00126283,0.001737023,0.0007945073,0.0007046155,0.002720358,0.002336298,0.001712863,0.002725275],"category_scores_gemma":[0.003509578,0.0007664148,0.001006142,0.001114534,0.002042335,0.003147827,0.00171985,0.001987984,0.0002144346],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001887655,"about_ca_system_score_gemma":0.001607612,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01142652,"about_ca_topic_score_gemma":0.008183338,"domain_scores_codex":[0.9992003,0.0003564534,0.00002820963,0.000131389,0.0001849733,0.00009864991],"domain_scores_gemma":[0.998715,0.0007140809,0.0002133021,0.00009081858,0.0001888168,0.00007788717],"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":[0.00001347309,0.00001884799,0.00009572601,0.00002406511,0.00003364824,0.00003068695,0.00001078544,0.9604974,0.0001287999,0.03648515,0.0002458899,0.00241543],"study_design_scores_gemma":[0.000003048658,0.000004057339,0.00004294677,0.000002141598,0.000004551802,0.000001572887,0.000002717777,0.9889517,0.00002157002,0.01084801,0.0001146936,0.000002900017],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03879276,0.001559762,0.9387498,0.002105071,0.0002794886,0.00005027339,0.0001870455,0.0001640686,0.01811178],"genre_scores_gemma":[0.9727589,0.001112374,0.02096914,0.0001221935,0.0002129866,0.00009359159,0.00009259177,0.00006301361,0.004575177],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01142652,"threshold_uncertainty_score":0.02271998,"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."}}