{"id":"W2896064754","doi":"10.1016/j.eneco.2018.10.005","title":"The value of forecasts: Quantifying the economic gains of accurate quarter-hourly electricity price forecasts","year":2018,"lang":"en","type":"article","venue":"Energy Economics","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":74,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Narodowe Centrum Nauki; Deutsche Forschungsgemeinschaft","keywords":"Economics; Electricity; Econometrics; Quarter (Canadian coin); Predictive power; Electricity price forecasting; Economic forecasting; Multivariate statistics; Electricity market; Consensus forecast; Spot contract; Novelty; Financial economics; Computer science; Futures contract","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004820098,0.0002442992,0.0003251872,0.00008950692,0.0002311411,0.00005834831,0.0005674259,0.0001108988,0.00002201737],"category_scores_gemma":[0.00003335603,0.0001795243,0.0001694992,0.0001363056,0.000191895,0.0002120125,0.00006818864,0.0001515432,0.000007461902],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001780253,"about_ca_system_score_gemma":0.00008229441,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004088942,"about_ca_topic_score_gemma":0.002540968,"domain_scores_codex":[0.9985251,0.00005196767,0.0006923686,0.0002156776,0.00005689533,0.0004579966],"domain_scores_gemma":[0.9985635,0.0004848698,0.0003230432,0.0005097935,0.00005310123,0.00006567763],"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":[0.0001628851,0.00004698875,0.003356207,0.00009532266,0.0008018839,0.000001730677,0.002305702,0.6178085,0.002319858,0.2976641,0.004237547,0.07119924],"study_design_scores_gemma":[0.0002872506,0.0001484345,0.000336164,0.0000342581,0.00002451354,0.00001205429,0.0001963681,0.9124373,0.04892763,0.002001068,0.03533822,0.000256775],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9762545,0.0005129349,0.005738557,0.00006921691,0.001251236,0.00008181417,0.00003295037,0.00007522324,0.01598353],"genre_scores_gemma":[0.9984297,0.0005863775,0.0003114527,0.00004616942,0.000459786,0.00001511082,0.000008879608,0.0000571024,0.00008542372],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2956631,"threshold_uncertainty_score":0.7320787,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0225185340838337,"score_gpt":0.2331074557315745,"score_spread":0.2105889216477408,"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."}}