{"id":"W4393388337","doi":"10.3390/en17071672","title":"Toward Prediction of Energy Consumption Peaks and Timestamping in Commercial Supermarkets Using Deep Learning","year":2024,"lang":"en","type":"article","venue":"Energies","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Deep learning; Energy consumption; Consumption (sociology); Computer science; Energy (signal processing); Artificial intelligence; Statistics; Engineering; Electrical engineering; Mathematics; Sociology","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":[0.0002572626,0.0000694257,0.00008853069,0.00005446365,0.00006765992,0.00002685702,0.00003706233,0.00004691537,0.00007744973],"category_scores_gemma":[0.00003186449,0.00007057377,0.00001966674,0.0001009984,0.0001059216,0.000194656,0.00008412188,0.00009727028,0.000002379248],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007451583,"about_ca_system_score_gemma":0.000004424065,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006844113,"about_ca_topic_score_gemma":0.00002931171,"domain_scores_codex":[0.9993696,0.00008017148,0.0001596626,0.0001502671,0.0001179691,0.0001223075],"domain_scores_gemma":[0.999814,0.0000883194,0.00002616116,0.00004495166,0.000002600044,0.00002391729],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002204934,0.00002034247,0.6055911,0.0001237693,0.00001329183,0.00001350758,0.00718616,0.1363764,0.02283311,0.0002591233,0.00003031813,0.2275309],"study_design_scores_gemma":[0.0002324996,0.00006631186,0.4129117,0.0004268478,0.000023822,0.00003122838,0.001022258,0.5774753,0.00536427,0.0001811184,0.002062218,0.0002023727],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9971004,0.0005426835,0.00134545,0.00001946777,0.0001825806,0.00001742306,0.000001617469,0.00005181814,0.0007385641],"genre_scores_gemma":[0.998966,0.0001514818,0.0007285622,0.00000486667,0.00006580909,0.000002308631,0.000005145012,0.00000865212,0.00006714791],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4410989,"threshold_uncertainty_score":0.2877915,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04359761656700591,"score_gpt":0.2635894012920011,"score_spread":0.2199917847249952,"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."}}