{"id":"W4414583580","doi":"10.1016/j.suscom.2025.101214","title":"A two-stage spatio-temporal flexibility-based energy optimization of internet data centers in active distribution networks based on robust control and transformer machine learning strategy","year":2025,"lang":"en","type":"article","venue":"Sustainable Computing Informatics and Systems","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Mean squared error; Robustness (evolution); Mean absolute percentage error; Approximation error; Feature selection; Efficient energy use; Extreme learning machine; Renewable energy; Gradient boosting","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":[0.0008732058,0.0008181896,0.001325325,0.000415036,0.0005990954,0.001355582,0.00133498,0.001139916,0.002316588],"category_scores_gemma":[0.0009407673,0.0005416173,0.0008336723,0.000487334,0.0007892001,0.001180759,0.001241111,0.0006461337,0.0001667736],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006791533,"about_ca_system_score_gemma":0.000934297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007335316,"about_ca_topic_score_gemma":0.005245627,"domain_scores_codex":[0.9996574,0.00008772311,0.00001955345,0.00008609509,0.00008129667,0.00006798618],"domain_scores_gemma":[0.9997184,0.0001309025,0.00003766406,0.00001695728,0.00007518605,0.00002086901],"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":[0.00007633131,0.00002655669,0.0002974956,0.00005265486,0.00002993393,0.00005915704,0.00003869397,0.9837151,0.001626592,0.004614758,0.0003140949,0.009148674],"study_design_scores_gemma":[0.000003550398,0.00001565282,0.00004599899,0.000001780848,0.000004887886,0.000005565355,0.00000480465,0.9992009,0.0001825263,0.0004679773,0.00006366825,0.000002742007],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03949167,0.0003314954,0.9536422,0.0002251428,0.00005377169,0.00006660174,0.00003892351,0.0001675123,0.00598271],"genre_scores_gemma":[0.9772117,0.000133104,0.01994356,0.00004303334,0.00001782015,0.00007362379,0.00003776493,0.00002891564,0.002510501],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007335316,"threshold_uncertainty_score":0.0145852,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01036138063028614,"score_gpt":0.2174166966273537,"score_spread":0.2070553159970676,"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."}}