{"id":"W2738343460","doi":"10.1109/iwcmc.2017.7986424","title":"Dynamic pricing model for EV charging-discharging service based on cloud computing scheduling","year":2017,"lang":"en","type":"preprint","venue":"","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Cloud computing; Computer science; Smart grid; Scheduling (production processes); Dynamic pricing; Electric vehicle; Grid; Schedule; Vehicle-to-grid; Distributed computing; Electricity pricing; Electricity; Power (physics); Mathematical optimization; Engineering; Electricity market; Electrical engineering; Operating system","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":[0.0003899009,0.0006462476,0.0006408502,0.000254533,0.0004574473,0.0003370357,0.0008149521,0.0005265735,0.00001701684],"category_scores_gemma":[0.00004898123,0.0006458341,0.000252697,0.0001076953,0.00001447113,0.0001104832,0.0001502273,0.001408297,0.00001073515],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003525098,"about_ca_system_score_gemma":0.0001432236,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003501674,"about_ca_topic_score_gemma":0.00002307121,"domain_scores_codex":[0.9977148,0.00001666634,0.0005025761,0.000688937,0.0002957664,0.0007812387],"domain_scores_gemma":[0.9983931,0.0001202621,0.0002429682,0.000943943,0.0001601809,0.000139504],"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":[0.00001265756,0.000009658605,0.00003475678,0.001472338,0.00006450655,0.000001578033,0.0002574922,0.9899606,0.002092268,0.0003424177,0.0001226446,0.005629139],"study_design_scores_gemma":[0.0005344931,0.00001837953,0.0000628912,0.001101321,0.00005934442,0.000002318226,0.00002603865,0.9946997,0.001198247,0.001516626,0.00006012749,0.00072052],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1069145,0.0002611153,0.8869922,0.0004280996,0.001122496,0.000790551,0.00005015058,0.000937462,0.002503411],"genre_scores_gemma":[0.8873042,0.00002299839,0.1112563,0.0005062949,0.0004218034,0.00003631481,0.0001471606,0.0001921121,0.0001127794],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7803897,"threshold_uncertainty_score":0.9995993,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01413335412376592,"score_gpt":0.2481792714632196,"score_spread":0.2340459173394536,"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."}}