{"id":"W4295064639","doi":"10.11113/aej.v12.17276","title":"NEXT-HOUR ELECTRICITY PRICE FORECASTING USING LEAST SQUARES SUPPORT VECTOR MACHINE AND GENETIC ALGORITHM","year":2022,"lang":"en","type":"article","venue":"ASEAN Engineering Journal","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Universiti Teknikal Malaysia Melaka","keywords":"Bidding; Electricity price forecasting; Electricity market; Mean absolute percentage error; Genetic algorithm; Support vector machine; Electricity; Computer science; Least squares support vector machine; Least-squares function approximation; Econometrics; Algorithm; Mathematical optimization; Artificial neural network; Machine learning; Economics; Statistics; Mathematics; Engineering; Microeconomics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004313388,0.0006038864,0.0008253158,0.0007299578,0.0002869862,0.0006726912,0.0007479435,0.0007942212,0.0007579576],"category_scores_gemma":[0.001425285,0.0003176008,0.0006528657,0.0006781071,0.0001755582,0.0006395034,0.0003255078,0.0007319831,0.0002235321],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005509854,"about_ca_system_score_gemma":0.0008919002,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02392019,"about_ca_topic_score_gemma":0.01556762,"domain_scores_codex":[0.9996924,0.00004872798,0.00002367131,0.00006946201,0.0001231554,0.00004262275],"domain_scores_gemma":[0.9996161,0.000167078,0.00005084548,0.00001807832,0.0001297958,0.00001799373],"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":[0.00006640158,0.00009026008,0.002216553,0.00003812544,0.00005294622,0.00007358682,0.00002580316,0.9062322,0.002171531,0.0006035117,0.0005380459,0.08789106],"study_design_scores_gemma":[0.000002043061,0.000009804496,0.000214885,0.000001033745,0.000002115275,0.000003431591,0.000002104091,0.9994352,0.0001820234,0.00009684187,0.00004823347,0.000002133178],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3316958,0.0009163916,0.6589562,0.0004097063,0.0001596653,0.0001146889,0.0001991468,0.001646165,0.005902205],"genre_scores_gemma":[0.9329605,0.0002067064,0.06466302,0.00004909571,0.00002849381,0.00007006821,0.000206875,0.00002480544,0.001790437],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02392019,"threshold_uncertainty_score":0.04756188,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01492146003949985,"score_gpt":0.1963336794711232,"score_spread":0.1814122194316234,"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."}}