{"id":"W4408777410","doi":"10.3390/s25072026","title":"Clustering and Interpretability of Residential Electricity Demand Profiles","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Interpretability; Cluster analysis; Electricity; Electricity demand; Demand response; Computer science; Data mining; Engineering; Artificial intelligence; Electricity generation; Electrical engineering; Power (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":[0.0001021989,0.00006737318,0.0001133001,0.00006797693,0.00002654536,0.00001013671,0.00004204586,0.00004118133,0.000008825409],"category_scores_gemma":[0.00006445612,0.00006645342,0.0000241964,0.0001163831,0.00003075635,0.00003113306,0.00003039075,0.00007488168,4.620727e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001573629,"about_ca_system_score_gemma":0.000007407071,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003954738,"about_ca_topic_score_gemma":0.00007298001,"domain_scores_codex":[0.9995907,0.00001978514,0.0001469721,0.0000899708,0.00004238541,0.0001101758],"domain_scores_gemma":[0.9997987,0.00006088843,0.00001436129,0.00008817636,0.00001398001,0.00002386615],"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":[0.0002566889,0.00007208304,0.140154,0.004672496,0.0004950283,0.00002441801,0.005301435,0.5277686,0.2420568,0.003501186,0.001204371,0.07449287],"study_design_scores_gemma":[0.0003754061,0.0000364373,0.02153141,0.0002579225,0.00004015906,0.000008636001,0.0001327279,0.6226916,0.3532141,0.0008115079,0.0006719283,0.0002281529],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9872892,0.0003517511,0.003990136,0.00001566578,0.0001711465,0.00004939793,0.000002578265,0.00008351511,0.008046565],"genre_scores_gemma":[0.9993511,0.00003363488,0.000466089,0.000006599154,0.00001900883,0.000001665437,9.137698e-7,0.000006073284,0.0001149527],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1186226,"threshold_uncertainty_score":0.2709892,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005032771326364237,"score_gpt":0.2125209381244601,"score_spread":0.2074881667980959,"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."}}