{"id":"W4389841718","doi":"10.1016/j.egyr.2023.12.031","title":"Unveiling the backbone of the renewable energy forecasting process: Exploring direct and indirect methods and their applications","year":2023,"lang":"en","type":"article","venue":"Energy Reports","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"United Nations University Institute for Water, Environment, and Health","funders":"Belgian Federal Science Policy Office","keywords":"Renewable energy; Process (computing); Computer science; Industrial engineering; Artificial intelligence; Data science; Machine learning; Engineering","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":[0.00188948,0.0007429658,0.0006919285,0.0008769016,0.0003701113,0.002217619,0.001152535,0.001061844,0.00204197],"category_scores_gemma":[0.007671085,0.0004110005,0.0006316332,0.000816216,0.001016114,0.003834598,0.001807317,0.002199677,0.0006190357],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003989029,"about_ca_system_score_gemma":0.0008708062,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001949953,"about_ca_topic_score_gemma":0.002220597,"domain_scores_codex":[0.9995283,0.0002116681,0.00002529826,0.00007504204,0.0001342627,0.00002549456],"domain_scores_gemma":[0.9963129,0.002670345,0.0002462042,0.0003632784,0.0003045651,0.0001027571],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009324553,0.0002125678,0.009633536,0.000809559,0.0001941908,0.0001884272,0.0006782978,0.2617244,0.004567631,0.2937697,0.002485982,0.4256425],"study_design_scores_gemma":[0.000008118255,0.00006262241,0.001308774,0.0001692396,0.0000301641,0.00007109432,0.0001210969,0.8359116,0.001376321,0.1548103,0.006099688,0.0000310818],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.02631193,0.005764958,0.9561355,0.002614422,0.0001565007,0.00003537616,0.00007251328,0.0002159729,0.008692832],"genre_scores_gemma":[0.6312675,0.01558677,0.3451317,0.0003311801,0.0008176155,0.0001138447,0.0002561572,0.00019895,0.006296374],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.002217619,"threshold_uncertainty_score":0.009992659,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03750939064578147,"score_gpt":0.2496282704929756,"score_spread":0.2121188798471941,"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."}}