{"id":"W4391774748","doi":"10.1016/j.segan.2024.101319","title":"A deep clustering framework for load pattern segmentation","year":2024,"lang":"en","type":"article","venue":"Sustainable Energy Grids and Networks","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Research Foundation of Korea; Ministry of Education; CHEO Research Institute","keywords":"Cluster analysis; Computer science; Autoencoder; Profiling (computer programming); Dimensionality reduction; Bottleneck; Data mining; Smart grid; Smart meter; Big data; Artificial intelligence; Machine learning; Deep learning; Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001502557,0.0001874054,0.0001455646,0.00007210658,0.0001454457,0.0002213901,0.00006811419,0.0001531424,0.00002256514],"category_scores_gemma":[0.00001116518,0.0001819546,0.00006083361,0.0002041344,0.00002035507,0.0001936096,0.00004670581,0.0001589559,6.429227e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001234628,"about_ca_system_score_gemma":0.00001974634,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008109021,"about_ca_topic_score_gemma":0.00006953443,"domain_scores_codex":[0.9989983,0.00000990075,0.0001782335,0.0002234286,0.00008881355,0.0005012595],"domain_scores_gemma":[0.9995992,0.000149954,0.00001438508,0.0001081131,0.00004222953,0.00008610787],"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":[0.000008264112,0.000003563521,0.00003536456,0.0003798175,0.00005700055,0.00004080178,0.0003129684,0.7502809,0.00001466739,0.02439758,0.0007698854,0.2236992],"study_design_scores_gemma":[0.0001306028,0.00004587958,0.00001045775,0.000180716,0.00002401019,0.00001128337,0.0004676041,0.9467654,0.0001106936,0.005507401,0.04652066,0.0002252497],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002485136,0.01550254,0.9788631,0.00004040977,0.00104959,0.00008220493,0.000001790715,0.0003842031,0.001591007],"genre_scores_gemma":[0.9926086,0.001287073,0.003165175,0.0001157336,0.001285074,0.0001334327,0.00003803401,0.0000806649,0.001286204],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9901235,"threshold_uncertainty_score":0.7419894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005344232620995314,"score_gpt":0.2141001998455805,"score_spread":0.2087559672245851,"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."}}