{"id":"W2125765712","doi":"10.1109/pesc.2005.1581917","title":"Energy Management Strategies for Optimization of Energy Storage in Wind Power Hybrid System","year":2006,"lang":"en","type":"article","venue":"","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Resources Canada","keywords":"Wind power; Energy storage; Computer science; Intermittent energy source; Renewable energy; Grid; Energy management; Pumped-storage hydroelectricity; Computer data storage; Automotive engineering; Reliability engineering; Energy (signal processing); Distributed generation; Electrical engineering; Power (physics); Engineering; Computer hardware","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002865537,0.0005277771,0.0004560589,0.0003567828,0.0003081573,0.0009201281,0.0004275065,0.0005308072,0.002471253],"category_scores_gemma":[0.0003609206,0.0001998078,0.000165526,0.0002451758,0.000250089,0.000532911,0.0003166198,0.0002386629,0.0002290721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004033918,"about_ca_system_score_gemma":0.0003222881,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002034907,"about_ca_topic_score_gemma":0.003419126,"domain_scores_codex":[0.9999064,0.00002872505,0.000006546694,0.000013434,0.00003085642,0.00001403648],"domain_scores_gemma":[0.9999155,0.00003086658,0.00001888516,0.000003471037,0.00002665687,0.000004595467],"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":[0.0001933885,0.00009165473,0.0005138558,0.0001352,0.00004505056,0.0001606187,0.0001225616,0.90984,0.01623479,0.01012221,0.0009727326,0.06156809],"study_design_scores_gemma":[0.00003574206,0.0001180819,0.0003310432,0.00001611104,0.00002073822,0.00002822265,0.00004926448,0.9935485,0.002236031,0.002679855,0.0009284929,0.000008072449],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1843607,0.001814775,0.7807453,0.0005823633,0.0000719595,0.0003191712,0.0001172966,0.000523371,0.03146524],"genre_scores_gemma":[0.9835719,0.00022791,0.01312488,0.00003928447,0.00001433769,0.000109152,0.00002320272,0.00001411321,0.002875197],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002471253,"threshold_uncertainty_score":0.008267105,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006839742246648619,"score_gpt":0.2163827069662429,"score_spread":0.2095429647195943,"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."}}