{"id":"W4402477145","doi":"10.11159/icert24.102","title":"Hourly Hydropower Production Forecasting with Machine Learning: A Case Study in Linköping, Sweden","year":2024,"lang":"en","type":"article","venue":"Proceedings of the World Congress on New Technologies","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Hydropower; Ping (video games); Link (geometry); Computer science; Production (economics); Artificial intelligence; Machine learning; Engineering; Electrical engineering; Computer network; Economics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002345191,0.0002699682,0.000257818,0.0006060006,0.000103199,0.0001264747,0.0003759904,0.00008483673,0.000003629978],"category_scores_gemma":[0.0002050225,0.0001795264,0.00005365264,0.001433759,0.0001001578,0.0002564625,0.0001523738,0.0008823484,0.000001753941],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008126836,"about_ca_system_score_gemma":0.00001843449,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001639684,"about_ca_topic_score_gemma":0.001303004,"domain_scores_codex":[0.99881,0.000004903547,0.0002987696,0.0003494773,0.0002268682,0.0003100073],"domain_scores_gemma":[0.9996229,0.00006119568,0.00009147995,0.0001555503,0.00004405442,0.00002486408],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000364498,0.0005460706,0.3940124,0.002413382,0.001063622,0.002492477,0.01118258,0.1336107,0.01440528,0.005127922,0.009478974,0.4253021],"study_design_scores_gemma":[0.003126338,0.003032009,0.0008288447,0.01939029,0.000452015,0.006520832,0.04025539,0.3601184,0.5224893,0.003817003,0.0370058,0.002963722],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9915169,0.0009370595,0.000005058188,0.0006051895,0.0006648083,0.0004116068,0.000001379554,0.002323845,0.003534087],"genre_scores_gemma":[0.9964393,0.00003346189,0.0003821879,0.000003422422,0.0000635245,0.00004707299,4.666716e-7,0.00006093618,0.002969644],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.508084,"threshold_uncertainty_score":0.7320872,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02083193061467114,"score_gpt":0.2393109833069367,"score_spread":0.2184790526922656,"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."}}