{"id":"W1643103419","doi":"10.1109/ccece.1995.528149","title":"Real-time electricity pricing using a neural network approach","year":2002,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Technical University of Nova Scotia","funders":"","keywords":"Artificial neural network; Backpropagation; Electricity; Computer science; Feedforward neural network; Electric power system; Spot contract; Electricity market; Electricity pricing; Feed forward; Artificial intelligence; Power (physics); Engineering; Control engineering; Economics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008680196,0.0001459995,0.0001552307,0.0000495787,0.00009655528,0.00003713792,0.00009328919,0.00006497575,0.0002000418],"category_scores_gemma":[0.000007019,0.0001373229,0.00005138428,0.0004060419,0.000009674382,0.0001161266,0.00002051425,0.0001459328,0.00002742883],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004806012,"about_ca_system_score_gemma":0.000002164799,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005787208,"about_ca_topic_score_gemma":0.000001800594,"domain_scores_codex":[0.9991288,0.00001739449,0.0001711249,0.0001421674,0.0001010102,0.0004395063],"domain_scores_gemma":[0.9997265,0.00003721027,0.00001957761,0.0001357885,0.00001183671,0.00006910742],"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":[8.899729e-7,0.00000882427,0.0003213393,0.00001461283,0.00001621683,0.000003551985,0.00009237607,0.9899169,0.005021141,0.0002108366,0.001545425,0.002847886],"study_design_scores_gemma":[0.00008559367,0.00001066094,0.00007834689,0.00001125601,0.00000972421,0.00002936578,0.000004081733,0.9987002,0.0005236785,0.00001901473,0.0003542252,0.0001738225],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6499655,0.0002455165,0.01203946,0.000004585646,0.000172069,0.00006669078,4.173185e-7,0.0008265064,0.3366793],"genre_scores_gemma":[0.9755415,0.00003883079,0.02297961,0.00002791678,0.000435948,0.000002582326,0.000003014522,0.00004302203,0.0009275263],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3357517,"threshold_uncertainty_score":0.5599867,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02074232885734734,"score_gpt":0.197014736788108,"score_spread":0.1762724079307607,"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."}}