{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002901084,0.0006786964,0.0005304362,0.002666252,0.0004083075,0.001953478,0.0005683578,0.0006202308,0.0007953721],"category_scores_gemma":[0.014541,0.0002318384,0.0005550242,0.001595452,0.0005004802,0.00143403,0.0008828376,0.0007557437,0.000324867],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007867789,"about_ca_system_score_gemma":0.0005913007,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006846031,"about_ca_topic_score_gemma":0.004976399,"domain_scores_codex":[0.9984174,0.0006704549,0.0001243367,0.0002905151,0.0003886415,0.0001086338],"domain_scores_gemma":[0.9952561,0.002681642,0.0006412005,0.0004450663,0.0009040044,0.00007210218],"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":[0.001319955,0.0002459825,0.1224734,0.0003488759,0.0002348966,0.0005221866,0.003801631,0.521454,0.01198525,0.01267812,0.004750161,0.3201857],"study_design_scores_gemma":[0.00001572288,0.00006032332,0.0409465,0.00006470909,0.00003301734,0.00009890962,0.0013444,0.9415306,0.004141628,0.01024543,0.001472123,0.00004648351],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.768478,0.0002976267,0.2229078,0.0005044775,0.00004772466,0.0001693856,0.001609024,0.0008384034,0.00514754],"genre_scores_gemma":[0.9703535,0.000104407,0.02773976,0.00002628805,0.00001264692,0.00003427101,0.001295396,0.00004877875,0.0003850117],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006846031,"threshold_uncertainty_score":0.01534253,"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."}}