{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002841111,0.0006190809,0.0004517087,0.0007955138,0.0001754934,0.0005660076,0.0003097479,0.0003405487,0.0008645579],"category_scores_gemma":[0.001484601,0.0001290744,0.0002830221,0.0009011375,0.0001511112,0.0008238044,0.0003586646,0.0003808987,0.0005134338],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002139079,"about_ca_system_score_gemma":0.00016552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002069212,"about_ca_topic_score_gemma":0.001960773,"domain_scores_codex":[0.999782,0.00004750134,0.00001346015,0.00006562885,0.00006268133,0.00002864529],"domain_scores_gemma":[0.9996679,0.0001151768,0.00006670065,0.0000581617,0.00006692251,0.00002510188],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008662939,0.0003972012,0.09752049,0.0001559834,0.0001483447,0.0009048576,0.0004820553,0.3969815,0.03220709,0.003548172,0.004305043,0.462483],"study_design_scores_gemma":[0.000002636953,0.00004428386,0.006250558,0.000004623457,0.000006737176,0.00009852614,0.00004759663,0.9893566,0.002958093,0.0008257679,0.0003953679,0.000009326138],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5362114,0.000214032,0.4578029,0.0003036723,0.00006599375,0.00008928792,0.0005196794,0.001473614,0.003319479],"genre_scores_gemma":[0.9749186,0.0001106862,0.02360442,0.00002650278,0.00001805975,0.00002971717,0.0003295874,0.00002836049,0.0009341975],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002069212,"threshold_uncertainty_score":0.004114389,"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."}}