{"id":"W4214807822","doi":"10.1109/access.2022.3156942","title":"A Review of Machine Learning-Based Photovoltaic Output Power Forecasting: Nordic Context","year":2022,"lang":"en","type":"review","venue":"IEEE Access","topic":"Solar Radiation and Photovoltaics","field":"Computer Science","cited_by":65,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Norges Teknisk-Naturvitenskapelige Universitet","keywords":"Computer science; Photovoltaic system; Context (archaeology); Power (physics); Artificial intelligence; Machine learning; Electrical engineering; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0008305355,0.0009366192,0.001143986,0.002824995,0.0002633443,0.001115131,0.001001635,0.00118164,0.002278154],"category_scores_gemma":[0.001817251,0.0004250547,0.0006584162,0.00484825,0.0003280928,0.001601643,0.0004389495,0.001199982,0.001350894],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006314068,"about_ca_system_score_gemma":0.001539144,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002627054,"about_ca_topic_score_gemma":0.002771131,"domain_scores_codex":[0.9997379,0.00005097092,0.00004410686,0.00006003665,0.0000900008,0.00001704844],"domain_scores_gemma":[0.9986421,0.0008430234,0.0001274931,0.00003100927,0.0003150626,0.00004115436],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004581863,0.0000756881,0.0004769677,0.02605725,0.0001492362,0.0001674167,0.00007789389,0.002648421,0.0008070976,0.00664633,0.03229243,0.9305554],"study_design_scores_gemma":[0.000008160243,0.0001316981,0.001561888,0.01270644,0.0002673816,0.0007405379,0.00009443285,0.001326355,0.000673589,0.004175824,0.9782647,0.00004891795],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0001628008,0.9968798,0.0008120117,0.0003309627,0.0002955883,0.00000550279,0.00003841496,0.00001320252,0.001461758],"genre_scores_gemma":[0.001222188,0.9971706,0.0007227475,0.000137014,0.0002802336,0.000006624933,0.00005853448,0.000003612061,0.0003984207],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.002824995,"threshold_uncertainty_score":0.007621169,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1325975445110196,"score_gpt":0.3493231658646645,"score_spread":0.2167256213536449,"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."}}