{"id":"W4281642482","doi":"10.3389/fenrg.2022.899692","title":"Hybrid Short-Term Wind Power Prediction Based on Markov Chain","year":2022,"lang":"en","type":"article","venue":"Frontiers in Energy Research","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Markov chain; Term (time); Wind power; Chaotic; Computer science; Markov model; Wind power forecasting; Markov process; Hidden Markov model; Variable-order Markov model; Power (physics); Engineering; Electric power system; Machine learning; Mathematics; Artificial intelligence; Statistics; 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":[0.0006485865,0.0006221662,0.0009973467,0.0006690459,0.0005058704,0.0007115015,0.0009523968,0.0005528081,0.001314506],"category_scores_gemma":[0.001513185,0.0005038009,0.0008206282,0.0006032743,0.0004379646,0.00163984,0.0007288706,0.0007248399,0.0002381824],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005466827,"about_ca_system_score_gemma":0.0009973443,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0144598,"about_ca_topic_score_gemma":0.009538347,"domain_scores_codex":[0.9994999,0.00008794833,0.00003422993,0.0001240355,0.0001793588,0.00007460079],"domain_scores_gemma":[0.9992967,0.0003697101,0.00007350573,0.00005981292,0.0001548136,0.0000454841],"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":[0.00009031199,0.00003683949,0.002522238,0.00004327555,0.00007123801,0.0000977509,0.0000564664,0.9541124,0.002415546,0.006050893,0.0003836691,0.03411943],"study_design_scores_gemma":[0.000002671855,0.000006003641,0.0001164106,0.000001385736,0.000003763893,0.000005566898,0.000001361761,0.9988728,0.0002048522,0.0007341161,0.00004750814,0.000003622045],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06735878,0.0003125448,0.9293888,0.0001405393,0.0000607624,0.00003408485,0.0001001643,0.0005264311,0.002077771],"genre_scores_gemma":[0.9579933,0.0002975272,0.03970933,0.00004213231,0.00004410823,0.00006869752,0.0002038045,0.00003496513,0.001606117],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0144598,"threshold_uncertainty_score":0.02875125,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01710911618406074,"score_gpt":0.2464981018028047,"score_spread":0.2293889856187439,"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."}}