{"id":"W4404281803","doi":"10.1007/s43621-024-00615-6","title":"Optimizing deep neural network architectures for renewable energy forecasting","year":2024,"lang":"en","type":"article","venue":"Discover Sustainability","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton","funders":"","keywords":"Renewable energy; Artificial neural network; Computer science; Artificial intelligence; Deep learning; Engineering; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000314737,0.0002893926,0.0002523264,0.00008625824,0.0001991435,0.0002347875,0.0001884484,0.0001019283,0.00002250864],"category_scores_gemma":[0.0001562302,0.0002654625,0.0002280691,0.0003582868,0.00006070263,0.0001555027,0.00008085041,0.0001907435,4.738173e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002697398,"about_ca_system_score_gemma":0.00007083405,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003560362,"about_ca_topic_score_gemma":0.0006233954,"domain_scores_codex":[0.9982851,0.00003739088,0.0003251043,0.0004046775,0.0001385255,0.0008092407],"domain_scores_gemma":[0.9990879,0.0004127748,0.00002310907,0.0002966816,0.00006990704,0.0001095843],"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":[0.00002380051,0.00000791705,0.0003987767,0.0006552807,0.00005019465,0.0000207096,0.0008199294,0.9607915,0.00001855239,0.002026283,0.0007455982,0.03444149],"study_design_scores_gemma":[0.0001307158,0.000045481,0.00005730291,0.00006542796,0.00003044392,0.00001334549,0.0002963718,0.9510018,0.0004328263,0.02827637,0.01933537,0.0003145364],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.28528,0.009352271,0.6907015,0.0001882764,0.00261541,0.000478728,0.0000370997,0.001601696,0.009744926],"genre_scores_gemma":[0.9953992,0.000005984441,0.003032027,0.00004986582,0.0008429159,0.000138321,0.00003765118,0.00008674058,0.0004073011],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7101192,"threshold_uncertainty_score":0.9999797,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01002785150113184,"score_gpt":0.2246987106249856,"score_spread":0.2146708591238538,"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."}}