{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001131664,0.0005683208,0.002078771,0.0004501971,0.0002128086,0.0002038286,0.003787225,0.0001855354,0.0009875841],"category_scores_gemma":[0.0006110038,0.0005004834,0.0009496215,0.001930185,0.00005954041,0.000383051,0.000504058,0.001067745,0.00006174226],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001799022,"about_ca_system_score_gemma":0.0009556253,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002295054,"about_ca_topic_score_gemma":0.00002323699,"domain_scores_codex":[0.9961622,0.0007186289,0.001212236,0.0007893196,0.0006979636,0.0004196757],"domain_scores_gemma":[0.9958381,0.0009183838,0.001644985,0.001239997,0.0001756803,0.00018287],"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":[0.000003497649,0.0001094193,0.00002992135,0.07601905,0.0001123165,0.0000413786,0.00006196964,0.00004373333,2.87797e-7,0.00004243012,0.01116835,0.9123676],"study_design_scores_gemma":[0.0002318302,0.0001115328,8.741398e-7,0.02489474,0.000187491,0.00004599685,0.000001101101,0.007903545,0.000008609101,0.00001762203,0.9661298,0.0004668399],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[8.842144e-7,0.9865744,0.009618036,0.00002967169,0.001350412,0.001290596,0.00007616417,0.0001765964,0.0008832793],"genre_scores_gemma":[0.00007660512,0.9962258,0.0002266214,0.002541075,0.00006466774,0.0003255041,0.0001537514,0.00007122667,0.0003146871],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9549615,"threshold_uncertainty_score":0.9999257,"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."}}