{"id":"W4226449636","doi":"10.1109/access.2022.3160484","title":"Solar Power Forecasting Using Deep Learning Techniques","year":2022,"lang":"en","type":"article","venue":"IEEE Access","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":220,"is_retracted":false,"has_abstract":true,"ca_institutions":"Saint Mary's University","funders":"","keywords":"Mean absolute percentage error; Photovoltaic system; Computer science; Artificial intelligence; Mean squared error; Perceptron; Deep learning; Artificial neural network; Machine learning; Multilayer perceptron; Electricity; Statistics; Engineering; Mathematics; Electrical 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":[0.0002694614,0.0005293836,0.0004674125,0.0005748955,0.0001626373,0.0006403719,0.0004564769,0.0005750681,0.001519744],"category_scores_gemma":[0.0009679574,0.0002844038,0.0003933138,0.0007981334,0.0001452408,0.0008030824,0.0003942953,0.0007899966,0.0004875475],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005510474,"about_ca_system_score_gemma":0.0004298853,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008676027,"about_ca_topic_score_gemma":0.009489868,"domain_scores_codex":[0.9998753,0.00001902573,0.00001057582,0.00002598283,0.00005094351,0.00001816412],"domain_scores_gemma":[0.9997515,0.0001136609,0.00003219593,0.00002111064,0.00007312594,0.000008344695],"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":[0.00002916024,0.00002981818,0.0009631526,0.00003736045,0.00002980805,0.0000316059,0.00001255657,0.9038338,0.002209248,0.001271609,0.001029491,0.09052245],"study_design_scores_gemma":[7.639028e-7,0.000002693144,0.0001386746,0.000002386137,0.000001168773,0.000002344551,0.000001157775,0.99879,0.0002689741,0.0006316053,0.0001589408,0.000001303647],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1114414,0.002057625,0.8747568,0.0005535638,0.0001657258,0.00003903362,0.0007292737,0.002141052,0.008115557],"genre_scores_gemma":[0.9196531,0.001041833,0.07357918,0.00009700186,0.00006908631,0.00004935634,0.000834213,0.0000533628,0.004622881],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008676027,"threshold_uncertainty_score":0.01725101,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03364399308681013,"score_gpt":0.2604231443911935,"score_spread":0.2267791513043833,"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."}}