{"id":"W4410403095","doi":"10.3390/electronics14102015","title":"Power Profiling of Smart Grid Users Using Dynamic Time Warping","year":2025,"lang":"en","type":"article","venue":"Electronics","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"MacEwan University","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea; National Research Foundation of Korea; National Research Foundation","keywords":"Profiling (computer programming); Image warping; Dynamic time warping; Smart grid; Computer science; Dynamic demand; Power grid; Embedded system; Real-time computing; Power (physics); Electrical engineering; Engineering; Operating system; Artificial intelligence; Physics","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":[],"consensus_categories":[],"category_scores_codex":[0.0002937419,0.0001140779,0.0002126543,0.0001471204,0.0001304287,0.00006412592,0.000456788,0.00004934108,0.00001720851],"category_scores_gemma":[0.00003473633,0.0001124509,0.000111021,0.00078531,0.00002719994,0.0002377867,0.0001888853,0.0001560296,0.000007256248],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001401396,"about_ca_system_score_gemma":0.0002280004,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006982187,"about_ca_topic_score_gemma":0.000008104968,"domain_scores_codex":[0.9988955,0.00003183317,0.0002755076,0.0002682648,0.0001512545,0.0003776278],"domain_scores_gemma":[0.9993586,0.00003982351,0.0001275004,0.000362622,0.00008283726,0.00002861109],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001063648,0.0003512857,0.005183215,0.0003057327,0.001299374,0.00003346294,0.001767083,0.05861316,0.5723509,0.2590048,0.0006605636,0.1003241],"study_design_scores_gemma":[0.000129465,0.0000781523,0.00007910633,0.00005703101,0.00002585444,0.000006935551,0.00002471823,0.9761242,0.01976049,0.001129694,0.002437117,0.0001472012],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.491411,0.004680374,0.4980634,0.0003056085,0.000335093,0.0001809176,0.000002377982,0.0001553738,0.004865827],"genre_scores_gemma":[0.9453185,0.0000617534,0.05374143,0.0001050039,0.00001891019,0.000002465868,0.0000043781,0.00001418681,0.0007333467],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.917511,"threshold_uncertainty_score":0.4585615,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006188824295193121,"score_gpt":0.2319595491051209,"score_spread":0.2257707248099278,"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."}}