{"id":"W4398131379","doi":"10.1016/j.compeleceng.2024.109305","title":"Power system flexibility analysis using net-load forecasting based on deep learning considering distributed energy sources and electric vehicles","year":2024,"lang":"en","type":"article","venue":"Computers & Electrical Engineering","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Flexibility (engineering); Net (polyhedron); Energy (signal processing); Electric power system; Computer science; Electric power; Power (physics); Distributed generation; Automotive engineering; Engineering; Industrial engineering; Artificial intelligence; Renewable energy; Electrical engineering; Mathematics; Statistics","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":[0.0003601648,0.0005103569,0.0006224745,0.0008478591,0.0002094706,0.000361367,0.0001813734,0.0001937908,0.000005659724],"category_scores_gemma":[0.0001185246,0.0005353779,0.0002580497,0.002878563,0.00002058617,0.0001768653,0.00006218201,0.0006778609,0.000001664869],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006972934,"about_ca_system_score_gemma":0.00004898835,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003724853,"about_ca_topic_score_gemma":0.000003045332,"domain_scores_codex":[0.9975323,0.00006430469,0.0005343287,0.000626378,0.0003721631,0.0008705415],"domain_scores_gemma":[0.9984689,0.0009227172,0.0000515422,0.0002393197,0.00005790099,0.000259634],"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":[0.000009970424,0.000009728916,0.0003694386,0.0002135219,0.0003742779,0.0001025833,0.0001112895,0.9810298,0.004557144,0.0006231749,0.000008505654,0.01259057],"study_design_scores_gemma":[0.0001962121,0.0000919645,0.0002112204,0.0003503997,0.0002199898,0.00006615371,0.00001583301,0.9947305,0.002878975,0.000008417481,0.0006865684,0.000543819],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3650934,0.003866445,0.6288607,0.000005624053,0.0002901169,0.00006097044,0.000002912669,0.001668264,0.0001515576],"genre_scores_gemma":[0.9949531,0.00002253651,0.004685023,0.00001817848,0.0001815433,0.00001166655,0.00002055196,0.0001039557,0.00000346462],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6298596,"threshold_uncertainty_score":0.9997098,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009819648752273653,"score_gpt":0.193168809895167,"score_spread":0.1833491611428933,"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."}}