{"id":"W4410629681","doi":"10.1016/j.rineng.2025.105425","title":"A hybrid machine learning and optimization framework for energy forecasting and management","year":2025,"lang":"en","type":"article","venue":"Results in Engineering","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"International Development Research Centre; Botswana International University of Science and Technology","keywords":"Computer science; Energy (signal processing); Energy management; Artificial intelligence; Machine learning; Industrial engineering; Management science; Engineering; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006807314,0.001004796,0.0007464148,0.0005099103,0.0002575402,0.0008786659,0.001162524,0.0008005194,0.001436791],"category_scores_gemma":[0.0009212111,0.0003570733,0.000644922,0.000834677,0.0003926228,0.0008512816,0.0006386288,0.001135573,0.000362309],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007495307,"about_ca_system_score_gemma":0.00120519,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009830533,"about_ca_topic_score_gemma":0.009708975,"domain_scores_codex":[0.9997012,0.00009056187,0.00001621681,0.00005658042,0.0001092974,0.00002611467],"domain_scores_gemma":[0.9997876,0.00009431959,0.00002636078,0.00002074242,0.00006033628,0.00001065097],"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":[0.00000786023,0.00001564324,0.0001287702,0.00002260729,0.00002504524,0.00001461598,0.000007474963,0.9663398,0.0005258501,0.01117434,0.000595174,0.02114288],"study_design_scores_gemma":[8.206468e-7,0.00000304078,0.00001466109,0.000001248602,0.00000137139,0.000001735332,8.711323e-7,0.9977702,0.00006527156,0.001830214,0.0003094892,0.000001181982],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0019807,0.0002584963,0.9956698,0.0001355543,0.00002675184,0.00001519723,0.00004598105,0.0002422055,0.001625217],"genre_scores_gemma":[0.4464936,0.001118877,0.5461379,0.0002042389,0.0001791417,0.0002521168,0.0004024831,0.0001511425,0.005060358],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009830533,"threshold_uncertainty_score":0.01954663,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006953919177652187,"score_gpt":0.2046102709511928,"score_spread":0.1976563517735406,"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."}}