{"id":"W4366764945","doi":"10.1016/j.enconman.2023.117063","title":"AI-coherent data-driven forecasting model for a combined cycle power plant","year":2023,"lang":"en","type":"article","venue":"Energy Conversion and Management","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Context (archaeology); Broyden–Fletcher–Goldfarb–Shanno algorithm; Electricity generation; Industrial engineering; Machine learning; Data mining; Artificial intelligence; Power (physics); Engineering","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":[0.0003212877,0.0004687077,0.0006255648,0.0003494781,0.0004614679,0.0008158937,0.000843862,0.001258588,0.002517522],"category_scores_gemma":[0.001070266,0.0003874425,0.0004129636,0.0007159536,0.000339365,0.000742052,0.0004230465,0.001171925,0.0003785901],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007851667,"about_ca_system_score_gemma":0.001022537,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04407981,"about_ca_topic_score_gemma":0.02849767,"domain_scores_codex":[0.9998854,0.00001874487,0.00000678935,0.00004652884,0.00002499425,0.00001742854],"domain_scores_gemma":[0.9996854,0.0001344985,0.00003925583,0.0000206814,0.0000937478,0.00002644974],"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":[0.00002690887,0.00001102135,0.0003077936,0.000009651772,0.000008264026,0.00001996161,0.000008412181,0.994882,0.0003309824,0.000853916,0.0003579529,0.00318319],"study_design_scores_gemma":[0.000001808016,0.000002329078,0.00006204992,4.696311e-7,0.000001296511,0.000001305707,8.122216e-7,0.9996561,0.00003471993,0.0001969422,0.00004078829,0.000001454557],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4402138,0.0007689937,0.5224871,0.002060035,0.000446743,0.0001213487,0.003852477,0.002729277,0.02732016],"genre_scores_gemma":[0.987637,0.00008783402,0.007977844,0.0000707494,0.0000349744,0.00004332148,0.0004917783,0.00002971009,0.003626749],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04407981,"threshold_uncertainty_score":0.08764648,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03314082118030349,"score_gpt":0.2246267724534142,"score_spread":0.1914859512731107,"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."}}