{"id":"W4414184187","doi":"10.38088/jise.1635104","title":"Short-Term Electricity Load Forecasting and Seasonality Analysis Using Temperature and Artificial Intelligence Methods in the Southeastern Anatolia Region","year":2025,"lang":"en","type":"article","venue":"Journal of Innovative Science and Engineering (JISE)","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Safran Electronics (Canada)","funders":"","keywords":"Autoregressive integrated moving average; Artificial neural network; Electricity; Energy consumption; Demand forecasting; Linear regression; Time series; Seasonality","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":[0.0004699109,0.0002326609,0.0002112802,0.0008488059,0.0001803101,0.000599003,0.000316784,0.0002746052,0.0003022942],"category_scores_gemma":[0.0006478442,0.0001093682,0.0002838268,0.001063058,0.0001033608,0.0005327274,0.0002130965,0.0001883729,0.00008182025],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005362802,"about_ca_system_score_gemma":0.0005347969,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04041034,"about_ca_topic_score_gemma":0.04720385,"domain_scores_codex":[0.9997829,0.00007112532,0.00003266712,0.00005149361,0.00003857624,0.00002327176],"domain_scores_gemma":[0.9996886,0.00009242204,0.00007135762,0.0000243756,0.0001106967,0.00001252107],"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":[0.0003766999,0.0002454974,0.6340187,0.0003000854,0.0004341752,0.001354585,0.0009290864,0.1566843,0.01401064,0.001791953,0.00198457,0.1878698],"study_design_scores_gemma":[0.00001118437,0.00007715268,0.5655205,0.00009315074,0.0001122289,0.0002428117,0.001833815,0.4234192,0.004626793,0.0005980293,0.003424683,0.00004038871],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9938828,0.0005094284,0.003361348,0.0001634845,0.00001700054,0.00001104713,0.0004015377,0.00004221403,0.001611152],"genre_scores_gemma":[0.9970048,0.0002218885,0.001998842,0.000007772573,0.000004785772,0.000009103365,0.0004210797,0.000005032239,0.0003266874],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04041034,"threshold_uncertainty_score":0.08035028,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04514255134966118,"score_gpt":0.3221319388582372,"score_spread":0.276989387508576,"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."}}