{"id":"W4407626438","doi":"10.1016/j.renene.2025.122692","title":"A reinforcement learning-based ensemble forecasting framework for renewable energy forecasting","year":2025,"lang":"en","type":"article","venue":"Renewable Energy","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Reinforcement learning; Renewable energy; Probabilistic forecasting; Ensemble learning; Computer science; Artificial intelligence; Demand forecasting; Technology forecasting; Machine learning; Engineering; Operations research","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":[0.001155584,0.0006919516,0.0009623061,0.0005002093,0.0003389552,0.0005471277,0.001091415,0.0005345152,0.001107644],"category_scores_gemma":[0.001993308,0.0002903127,0.0005955904,0.0005155037,0.0002712472,0.0007730906,0.000654312,0.001067325,0.0002252859],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006193409,"about_ca_system_score_gemma":0.0009641604,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01634119,"about_ca_topic_score_gemma":0.01325083,"domain_scores_codex":[0.9996184,0.00011616,0.00002336281,0.0000831301,0.0001049337,0.00005391312],"domain_scores_gemma":[0.9994298,0.0002269176,0.000064809,0.00004017832,0.0002007912,0.0000375799],"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":[0.00004459361,0.00004570753,0.001125942,0.00002127807,0.00005594439,0.00003842751,0.00003089919,0.9345704,0.0008686013,0.003129418,0.0007413672,0.05932733],"study_design_scores_gemma":[0.000001722689,0.000006852485,0.00005706841,0.000001115254,0.000003171222,0.000002425478,0.000001009483,0.9992579,0.00006881585,0.0005071004,0.00009103926,0.000001731418],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02125748,0.0003890688,0.9761574,0.0001428542,0.00006126465,0.00003261128,0.00007512131,0.0004540001,0.00143003],"genre_scores_gemma":[0.8589362,0.0004045056,0.1380481,0.0001028745,0.0000932754,0.000144179,0.0002783871,0.00005021658,0.001942232],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01634119,"threshold_uncertainty_score":0.0324921,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01925103389485812,"score_gpt":0.225298825336962,"score_spread":0.2060477914421038,"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."}}