{"id":"W4415744328","doi":"10.1109/ihtc65087.2025.11216310","title":"Integrating Machine Learning into Residential Energy Forecasting and Management","year":2025,"lang":"","type":"article","venue":"","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University of Edmonton","funders":"","keywords":"Retrofitting; Energy consumption; Usability; Random forest; Software; Energy (signal processing); Scalability; Efficient energy use; Demand forecasting","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":[0.0008764667,0.0005574562,0.0005463312,0.000535658,0.0002721468,0.0008183947,0.0008520436,0.0006076021,0.001720965],"category_scores_gemma":[0.002537698,0.0002581824,0.0004167419,0.0007181485,0.0002589493,0.001487056,0.0006492841,0.0007838667,0.0005953449],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000597648,"about_ca_system_score_gemma":0.000826655,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01153507,"about_ca_topic_score_gemma":0.01298641,"domain_scores_codex":[0.9996843,0.00009116145,0.00001917348,0.00008346662,0.00009209522,0.00002975637],"domain_scores_gemma":[0.999318,0.000358878,0.00006059522,0.00009574303,0.0001392852,0.00002752481],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002659469,0.00007679092,0.002488382,0.00005518198,0.00004584053,0.00004310576,0.00002960672,0.8338743,0.0009551342,0.005153334,0.001994156,0.1552576],"study_design_scores_gemma":[0.000001368251,0.000005416699,0.0001603143,0.000005369945,0.000003356733,0.000003611246,0.000005941879,0.9943725,0.0002653534,0.004408084,0.0007655559,0.000003178876],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02371581,0.0005531862,0.9678741,0.0006584813,0.00007325794,0.00005161659,0.0002264896,0.002194134,0.004652958],"genre_scores_gemma":[0.66197,0.0008872297,0.3327419,0.0002258221,0.000168874,0.0001120113,0.0006954678,0.0001675411,0.003031177],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01153507,"threshold_uncertainty_score":0.02293587,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005931360814465907,"score_gpt":0.1994458198301295,"score_spread":0.1935144590156636,"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."}}