{"id":"W2150180277","doi":"10.1002/er.3171","title":"Forecasting aggregated wind power production of multiple wind farms using hybrid wavelet-PSO-NNs","year":2014,"lang":"en","type":"article","venue":"International Journal of Energy Research","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Particle swarm optimization; Intermittency; Wind power; Wind power forecasting; Wind speed; Artificial neural network; Benchmark (surveying); Hybrid power; Computer science; Power (physics); Electric power system; Meteorology; Engineering; Algorithm; Artificial intelligence; Geography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004131109,0.0005693932,0.0007020988,0.0003409208,0.000166208,0.0005793609,0.0005384071,0.0005778667,0.0004707551],"category_scores_gemma":[0.001220394,0.0003334688,0.0004340788,0.0004778821,0.0001864043,0.0007058433,0.0003305373,0.0005013112,0.0001134478],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002518419,"about_ca_system_score_gemma":0.0003600018,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005743394,"about_ca_topic_score_gemma":0.006037023,"domain_scores_codex":[0.9998684,0.00002123072,0.00001170751,0.0000339983,0.0000494397,0.00001517656],"domain_scores_gemma":[0.9997339,0.0001345974,0.0000471679,0.00001962643,0.00005001718,0.00001460007],"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":[0.0000239981,0.00001993307,0.0008944279,0.00001891017,0.00002554964,0.00003582034,0.00001098119,0.9780572,0.0009471814,0.0003938526,0.0001243695,0.01944778],"study_design_scores_gemma":[0.0000012055,0.000004316937,0.0001067675,5.554405e-7,0.000001315558,0.000001848286,0.000001224229,0.9996911,0.00007230609,0.00009784618,0.00002089657,6.450963e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2521839,0.0003672145,0.7442083,0.0001604835,0.00009090339,0.0000423053,0.0001052729,0.0003888076,0.002452731],"genre_scores_gemma":[0.9382598,0.0001432988,0.06033924,0.00002528796,0.00002837491,0.00004002397,0.0001113361,0.00001894482,0.00103376],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005743394,"threshold_uncertainty_score":0.01141989,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05616668191409988,"score_gpt":0.3027093758806047,"score_spread":0.2465426939665048,"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."}}