{"id":"W7110177874","doi":"10.1155/etep/8828851","title":"Machine Learning‐Assisted Renewable Energy Uncertainty Compensation With Demand Response: An Analysis of Ship Energy Systems","year":2025,"lang":"en","type":"article","venue":"International Transactions on Electrical Energy Systems","topic":"Maritime Transport Emissions and Efficiency","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"University of Michigan-Dearborn; University of Michigan; National Science Foundation","keywords":"Demand response; Photovoltaic system; Renewable energy; Scheduling (production processes); Particle swarm optimization; Electric power system; Load profile; Compensation (psychology); Tariff","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":[0.0005773973,0.0004980189,0.0004424217,0.0004225635,0.0002511928,0.0005158291,0.0004184982,0.0004815304,0.001191669],"category_scores_gemma":[0.001153518,0.0002406231,0.0006269669,0.0004279781,0.0002186273,0.0004945582,0.00029928,0.0005292593,0.0001187662],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005394208,"about_ca_system_score_gemma":0.000502066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01185611,"about_ca_topic_score_gemma":0.005844356,"domain_scores_codex":[0.9998355,0.00005148571,0.0000084626,0.00002340198,0.00005062166,0.00003044943],"domain_scores_gemma":[0.9996296,0.0001978189,0.00005284483,0.00002241749,0.00008111459,0.00001619318],"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":[0.00001495254,0.00001403365,0.001011396,0.00001876587,0.00001715726,0.00006163523,0.00001033008,0.9931399,0.0004595923,0.001006507,0.0001573872,0.004088275],"study_design_scores_gemma":[4.283797e-7,0.000002798071,0.0003443504,6.4899e-7,9.992914e-7,0.000002597939,0.000001828122,0.9994299,0.00003848965,0.0001448943,0.00003232763,8.376053e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6333847,0.001073162,0.3475621,0.000819319,0.00006743636,0.0001148641,0.0003529509,0.00034474,0.01628075],"genre_scores_gemma":[0.9959615,0.0001075852,0.003068505,0.00001492857,0.00001101453,0.00001660668,0.00006012338,0.00001245339,0.0007472532],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01185611,"threshold_uncertainty_score":0.02357417,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008210369391774256,"score_gpt":0.2284916360968795,"score_spread":0.2202812667051053,"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."}}