{"id":"W3081576544","doi":"10.3390/su12177076","title":"Short-Term Load Forecasting of Microgrid via Hybrid Support Vector Regression and Long Short-Term Memory Algorithms","year":2020,"lang":"en","type":"article","venue":"Sustainability","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":158,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Support vector machine; Term (time); Computer science; Long short term memory; Microgrid; Artificial intelligence; Machine learning; Artificial neural network; Regression; Algorithm; Data mining; Recurrent neural network; Statistics; Mathematics","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":[0.0006707474,0.0007207168,0.000679886,0.0005081258,0.0001860632,0.0005680426,0.0006186984,0.0005664976,0.0006696874],"category_scores_gemma":[0.001445471,0.0002908913,0.0004944015,0.0006692178,0.0001539167,0.0009344189,0.0003906912,0.0006652844,0.0002214928],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002970483,"about_ca_system_score_gemma":0.0003962948,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007417316,"about_ca_topic_score_gemma":0.005892197,"domain_scores_codex":[0.9997529,0.0000671933,0.00002078134,0.00005978365,0.00006745043,0.00003190055],"domain_scores_gemma":[0.9995888,0.0001930651,0.00007016557,0.00002489203,0.0001096096,0.00001343872],"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":[0.0001026345,0.00007024017,0.002336365,0.0000539812,0.00008606745,0.0000723367,0.00003661992,0.8707602,0.002454353,0.001027954,0.0009255779,0.1220736],"study_design_scores_gemma":[0.000001072154,0.00000882698,0.0001313862,0.000001434311,0.000002454614,0.000003152899,0.000002486212,0.9994562,0.0001715895,0.0001712011,0.00004858243,0.000001611405],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1967711,0.001138516,0.7972088,0.0003574559,0.00008959018,0.00003737086,0.0001756024,0.001579133,0.002642363],"genre_scores_gemma":[0.952938,0.0002875102,0.04475503,0.00005252844,0.00004288842,0.0000448322,0.0002091815,0.0000360475,0.001634011],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007417316,"threshold_uncertainty_score":0.01474828,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01727014048706359,"score_gpt":0.2432673267034357,"score_spread":0.2259971862163721,"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."}}