{"id":"W4385880530","doi":"10.1016/j.iot.2023.100882","title":"Data-driven methods for the reduction of energy consumption in warehouses: Use-case driven analysis","year":2023,"lang":"en","type":"article","venue":"Internet of Things","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"HVAC; Energy consumption; Profiling (computer programming); Computer science; Air conditioning; Occupancy; Reduction (mathematics); Energy (signal processing); Control (management); Efficient energy use; Architectural engineering; Engineering; Artificial intelligence","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":[0.0003071023,0.00007252028,0.0001678176,0.0002717053,0.00001509168,0.0000155499,0.0002366373,0.00006240407,0.00001724287],"category_scores_gemma":[0.0000607344,0.00006184493,0.00006960001,0.0004394204,0.00003101807,0.0002808961,0.00007758701,0.00005784404,3.044712e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002565177,"about_ca_system_score_gemma":0.000004558545,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006225829,"about_ca_topic_score_gemma":0.0001351428,"domain_scores_codex":[0.9994141,0.00003989784,0.000263346,0.0001306012,0.0000622193,0.00008985405],"domain_scores_gemma":[0.9993073,0.0002501921,0.00008297049,0.0003138796,0.00003272136,0.00001294812],"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":[0.00001486077,0.000008935108,0.0004389146,0.0000360278,0.0003489211,0.000001843879,0.000585817,0.9879366,0.001096534,0.0007356046,0.0004876675,0.008308307],"study_design_scores_gemma":[0.0001091048,0.00001312891,0.0002194346,0.000031638,0.0001787218,0.000008789626,0.00006337804,0.993255,0.004784753,0.00007369794,0.001202653,0.00005972832],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1673019,0.00008610805,0.8320993,0.0000358881,0.0002483288,0.00006537748,0.00002032389,0.000125323,0.00001745448],"genre_scores_gemma":[0.9573793,0.000208525,0.04208922,0.0000091256,0.00001664013,0.00002079157,0.0001396176,0.000017545,0.0001192227],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7900774,"threshold_uncertainty_score":0.2521963,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06551004165648239,"score_gpt":0.3297173702809959,"score_spread":0.2642073286245135,"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."}}