{"id":"W2548151574","doi":"10.1109/ccece.2016.7726765","title":"Mid-term electricity price forecasting using SVM","year":2016,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Electricity market; Electricity price forecasting; Electricity; Purchasing; Term (time); Computer science; Time horizon; Support vector machine; Electricity price; Scheduling (production processes); Medium term; Operations research; Economics; Econometrics; Artificial intelligence; Engineering; Finance; Operations management","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":[0.0001108042,0.0001481083,0.0001295617,0.00008351441,0.00007370082,0.00002631786,0.0001142311,0.00006642052,0.0001937163],"category_scores_gemma":[0.00004659416,0.0001024664,0.00005366977,0.0002158339,0.00001472221,0.0002023416,0.00002865533,0.00007562051,0.00002682274],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000890369,"about_ca_system_score_gemma":0.00001233809,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001632909,"about_ca_topic_score_gemma":0.00001252686,"domain_scores_codex":[0.999134,0.00001053939,0.0001920519,0.0001489213,0.000108639,0.0004058491],"domain_scores_gemma":[0.9996156,0.0001069266,0.00002692376,0.0001413425,0.00002688042,0.00008234838],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001217799,0.00002564704,0.01339355,0.0001056901,0.0001009137,0.00003962439,0.0002315497,0.01289609,0.7759017,0.001491637,0.0006707031,0.1951307],"study_design_scores_gemma":[0.001022888,0.00006711621,0.001427186,0.0004194908,0.0000398175,0.0002025341,0.00002152913,0.3451521,0.641125,0.0005817118,0.008908815,0.001031931],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.825644,0.0001203078,0.11723,0.00001406076,0.0003553294,0.00004669006,0.000002066052,0.0005021876,0.05608536],"genre_scores_gemma":[0.9943489,0.00001726459,0.004745275,0.00002586132,0.000221157,0.000002668294,8.921733e-7,0.00003776615,0.0006002507],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.332256,"threshold_uncertainty_score":0.4178457,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02863933922905258,"score_gpt":0.2173522173576202,"score_spread":0.1887128781285676,"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."}}