{"id":"W6910864924","doi":"10.48550/arxiv.2008.02775","title":"Forecasting Photovoltaic Power Production using a Deep Learning Sequence to Sequence Model with Attention","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Seventeenth-Century Political and Philosophical Thought","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Deep learning; Photovoltaic system; Context (archaeology); Mean squared error; Sequence (biology); Time series; Artificial neural network; Baseline (sea); Power (physics)","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":[0.0003933115,0.0005999303,0.0005318581,0.0004160242,0.0002118545,0.0005732622,0.0008598643,0.000911487,0.001701084],"category_scores_gemma":[0.00115299,0.0003423076,0.0005550687,0.0005108913,0.0003179104,0.0009190741,0.000552276,0.001575239,0.0003634298],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008694835,"about_ca_system_score_gemma":0.0008209331,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01617124,"about_ca_topic_score_gemma":0.01861468,"domain_scores_codex":[0.9998771,0.00002154148,0.000005622154,0.00004797635,0.00002286111,0.00002487035],"domain_scores_gemma":[0.9996786,0.0001662268,0.00003617003,0.00002020732,0.00007516779,0.00002355317],"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":[0.00006631357,0.00006827213,0.001339297,0.00002422916,0.00003026739,0.00005811574,0.00002427396,0.9511229,0.001212145,0.002605621,0.001352175,0.04209641],"study_design_scores_gemma":[0.000001515888,0.000004358737,0.000065465,9.250632e-7,0.000001395981,0.000001971739,7.752586e-7,0.9991024,0.0001031128,0.0006639698,0.00005314806,9.677314e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2611484,0.001179371,0.7262482,0.001931966,0.0003320568,0.00006024409,0.0008970409,0.001836857,0.0063659],"genre_scores_gemma":[0.9603359,0.0003256495,0.03261777,0.000255121,0.0001049297,0.00005709902,0.0007662458,0.00003635625,0.005500946],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01617124,"threshold_uncertainty_score":0.03215426,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3265492020214931,"score_gpt":0.2302686790697529,"score_spread":0.09628052295174017,"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."}}