{"id":"W4319303160","doi":"10.1109/tcyb.2023.3234077","title":"A Condition Knowledge Representation and Feedback Learning Framework for Dynamic Optimization of Integrated Energy Systems","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Cybernetics","topic":"Integrated Energy Systems Optimization","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Key Research and Development Program of China; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Reinforcement learning; Computer science; Representation (politics); Scheduling (production processes); Partition (number theory); Mathematical optimization; Artificial intelligence; State-space representation; State space; Machine learning; Mathematics","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":[0.0008311721,0.0009509447,0.0009182186,0.0005259532,0.0003513398,0.0008923876,0.00122558,0.0009682315,0.002544632],"category_scores_gemma":[0.001801855,0.0003827459,0.0006112946,0.0005579001,0.0007477069,0.001384252,0.001072392,0.001529517,0.0002750333],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001182634,"about_ca_system_score_gemma":0.001397423,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.013916,"about_ca_topic_score_gemma":0.009327022,"domain_scores_codex":[0.9996647,0.00007638846,0.00001926745,0.0000864125,0.0001028903,0.00005043661],"domain_scores_gemma":[0.999604,0.0001740001,0.00005780129,0.00002860594,0.0001119741,0.00002350705],"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":[0.00001809937,0.00002205093,0.0001567151,0.0000221577,0.00001120573,0.00002457436,0.00001952114,0.9751659,0.0004898438,0.008306798,0.0003562269,0.01540699],"study_design_scores_gemma":[0.000001698869,0.000006176455,0.00001826881,0.00000136092,0.000001535463,0.000001726717,0.000001083221,0.9985089,0.0000644701,0.001308152,0.00008529391,0.000001405226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006332404,0.0001560039,0.9911221,0.0001192613,0.00002292235,0.00002485259,0.00003739636,0.0002192578,0.001965658],"genre_scores_gemma":[0.8800843,0.0003028085,0.1161422,0.0001236031,0.00006029728,0.0002681277,0.0002060271,0.00006708429,0.002745567],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.013916,"threshold_uncertainty_score":0.02767003,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01193885060399455,"score_gpt":0.2525841754484725,"score_spread":0.240645324844478,"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."}}