{"id":"W3193034144","doi":"10.1109/tii.2021.3097716","title":"Deep Spatial-Temporal 2-D CNN-BLSTM Model for Ultrashort-Term LiDAR-Assisted Wind Turbine's Power and Fatigue Load Forecasting","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund","keywords":"Lidar; Turbine; Wind power; Computer science; Deep learning; Convolutional neural network; Wind speed; Renewable energy; Artificial intelligence; Recurrent neural network; Artificial neural network; Simulation; Remote sensing; Meteorology; Engineering; Aerospace engineering; Geology; Electrical engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002414807,0.000901315,0.000480632,0.0003166204,0.0002336214,0.0004419422,0.001011286,0.0007875924,0.001871235],"category_scores_gemma":[0.0005416991,0.0003759273,0.0005592342,0.0004289422,0.0001921047,0.0008049353,0.0005195207,0.001108558,0.0005333595],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006548102,"about_ca_system_score_gemma":0.0009219914,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01796178,"about_ca_topic_score_gemma":0.02462199,"domain_scores_codex":[0.9999208,0.000005918718,0.000004761977,0.00002764108,0.00001884315,0.00002202916],"domain_scores_gemma":[0.999904,0.00002281342,0.00001323638,0.000009712749,0.00004056713,0.000009716882],"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":[0.0001524459,0.0001212804,0.002265039,0.00008545458,0.00006958388,0.0001439722,0.00003826448,0.8695359,0.009916228,0.002055458,0.004449868,0.1111667],"study_design_scores_gemma":[0.000001640042,0.000007184068,0.0001469898,0.000002143659,0.000003426825,0.000004306851,0.000001516853,0.9989871,0.0004154656,0.0003029263,0.0001255174,0.000001735359],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2543671,0.00348838,0.7247205,0.001186443,0.0004814217,0.00006886399,0.001520915,0.004703772,0.009462626],"genre_scores_gemma":[0.9609597,0.0005838648,0.03156246,0.0001709482,0.00005261026,0.00007332722,0.00115622,0.00006945872,0.005371425],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01796178,"threshold_uncertainty_score":0.03571445,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06418369217881079,"score_gpt":0.247514649757019,"score_spread":0.1833309575782082,"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."}}