{"id":"W4405720626","doi":"10.3390/en17246475","title":"Load Optimization for Connected Modern Buildings Using Deep Hybrid Machine Learning in Island Mode","year":2024,"lang":"en","type":"article","venue":"Energies","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Herzberg Institute of Astrophysics; University of Victoria","funders":"","keywords":"Mean absolute percentage error; Mean squared error; Photovoltaic system; Random forest; Smart grid; Computer science; Automotive engineering; Efficient energy use; Building automation; Artificial neural network; Real-time computing; Engineering; Artificial intelligence; Electrical engineering; Statistics; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0002518556,0.0009470355,0.0006671851,0.0003962659,0.0003316528,0.0007486694,0.0006780815,0.0007850616,0.00190048],"category_scores_gemma":[0.000460831,0.0004281226,0.0006022338,0.0003942814,0.0003173829,0.0006274176,0.0005090679,0.0005957246,0.0002953618],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001138405,"about_ca_system_score_gemma":0.000658437,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03779737,"about_ca_topic_score_gemma":0.0405613,"domain_scores_codex":[0.9999061,0.00001787604,0.000004083358,0.00002558491,0.00001760469,0.00002873038],"domain_scores_gemma":[0.9998685,0.00006613405,0.00001306476,0.000007125427,0.00003287495,0.000012222],"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":[0.00002537863,0.0000243162,0.0006780645,0.00001271576,0.00001384639,0.00002809967,0.000007620568,0.9868228,0.0003042432,0.0001975266,0.000294082,0.01159131],"study_design_scores_gemma":[0.000001078452,0.000003288003,0.00008339021,6.356086e-7,0.000001009851,0.000001024895,0.000002261167,0.9996974,0.00004462607,0.0001319504,0.00003259832,7.939791e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5354981,0.001295212,0.4450162,0.0007110795,0.0001116523,0.00007368613,0.0004761658,0.002409578,0.01440827],"genre_scores_gemma":[0.9872124,0.00007597155,0.0101336,0.00007211881,0.00001363828,0.00002526532,0.0002311822,0.0000366674,0.00219915],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03779737,"threshold_uncertainty_score":0.07515472,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008350669229516309,"score_gpt":0.2177736215798266,"score_spread":0.2094229523503103,"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."}}