{"id":"W4411434766","doi":"10.19184/cerimre.v8i1.53686","title":"Optimizing energy forecasts at Boma for 2023 to 2053 Using machine learning techniques of the PSO algorithm","year":2025,"lang":"en","type":"article","venue":"Computational And Experimental Research In Materials And Renewable Energy","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Cégep de l'Abitibi Témiscamingue; Université du Québec en Abitibi-Témiscamingue","funders":"","keywords":"Particle swarm optimization; Energy consumption; Computer science; Data collection; Consumption (sociology); Energy (signal processing); Machine learning; Correlation coefficient; Pearson product-moment correlation coefficient; Artificial intelligence; Environmental economics; Algorithm; Operations research; Statistics; Engineering; Economics; Mathematics","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":[0.0004311206,0.0004186723,0.0003156544,0.000444273,0.0002537312,0.0006421397,0.0002380153,0.0004519228,0.0005754083],"category_scores_gemma":[0.001435581,0.0001427255,0.000306293,0.0004601517,0.0001338469,0.0004582336,0.0001771258,0.0003763904,0.0001430623],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006533777,"about_ca_system_score_gemma":0.0006911386,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02521253,"about_ca_topic_score_gemma":0.02339489,"domain_scores_codex":[0.9999163,0.00003140048,0.000005166333,0.0000183196,0.00001578126,0.00001303973],"domain_scores_gemma":[0.9998307,0.00009861214,0.00002552985,0.000005903832,0.00003241235,0.000006771269],"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":[0.00002884318,0.00001476836,0.002738236,0.00002241009,0.00001858553,0.00003386987,0.00001803658,0.9794604,0.0004864935,0.0008550759,0.0003572947,0.01596603],"study_design_scores_gemma":[0.000002780977,0.00001394758,0.0009568834,0.00000621163,0.000004665883,0.000004294062,0.00001957387,0.9980193,0.0002543156,0.0003454536,0.0003694723,0.000003100162],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6035546,0.001227852,0.3778001,0.001127267,0.0001322437,0.0001260547,0.0008664776,0.0004757544,0.01468965],"genre_scores_gemma":[0.9692369,0.0002318084,0.02906416,0.0000275028,0.00001030293,0.00005239062,0.00022114,0.00002065605,0.00113507],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02521253,"threshold_uncertainty_score":0.05013156,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03125574644744362,"score_gpt":0.3160971164683667,"score_spread":0.2848413700209231,"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."}}