{"id":"W4404102683","doi":"10.1109/sege62220.2024.10739563","title":"Enhancing Wind Power Forecasting Accuracy in Canada Using a Solar Data-Enhanced Hybrid Machine Learning Model: Integrating ANN, LSTM, and SVR","year":2024,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Wind power; Machine learning; Artificial intelligence; Support vector machine; Data modeling; Power (physics); Artificial neural network; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0004928603,0.0003690727,0.0003547501,0.0002040331,0.0001684205,0.0002405743,0.0002562854,0.00006197898,0.0000656726],"category_scores_gemma":[0.0003570165,0.0003536865,0.0000352379,0.0003636745,0.00001931622,0.000962307,0.0002583528,0.000857878,0.000001526763],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005008832,"about_ca_system_score_gemma":0.0004769904,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.2720501,"about_ca_topic_score_gemma":0.694851,"domain_scores_codex":[0.9979678,0.00004001407,0.0005754844,0.0005306381,0.0002348267,0.0006512167],"domain_scores_gemma":[0.9990765,0.0004134954,0.00005407476,0.0002876422,0.0000317387,0.0001365806],"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":[0.000005492699,0.000002958814,0.0007195831,0.0002286205,0.00004943477,0.0001115904,0.00112581,0.9470192,0.02535114,0.00004210978,0.00006545213,0.02527866],"study_design_scores_gemma":[0.000152093,0.00001198268,0.0000131219,0.0008641175,0.00001866301,0.0000946248,0.0005526255,0.986141,0.0112949,0.00007195988,0.0003767732,0.0004081801],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8967406,0.002705004,0.09671084,0.00001473996,0.0004731338,0.0001162922,0.00005122426,0.0003477942,0.002840415],"genre_scores_gemma":[0.9891815,0.00006177397,0.01035304,0.00003992771,0.00009523953,0.000003174348,0.00009545671,0.000108621,0.00006129823],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.422801,"threshold_uncertainty_score":0.9998915,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02504936418227371,"score_gpt":0.2327546681013189,"score_spread":0.2077053039190452,"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."}}