{"id":"W4409583371","doi":"10.61091/jcmcc127a-006","title":"Research on Data-Driven Demand Forecasting and Service Optimisation Model for Electricity Users’ Behaviours","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Electricity demand; Electricity; Demand forecasting; Service (business); Service model; Computer science; Environmental economics; Operations research; Business; Economics; Electricity generation; Marketing; Engineering; Power (physics); Electrical engineering","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":[0.002492784,0.0002332399,0.0005052215,0.0003587714,0.0004144411,0.0002420929,0.0004256165,0.0001787296,5.315787e-7],"category_scores_gemma":[0.0003932075,0.0002252244,0.00005967649,0.0004418282,0.00004311053,0.0002396085,0.0002327045,0.0006055032,1.533326e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001020336,"about_ca_system_score_gemma":0.0001075106,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007564634,"about_ca_topic_score_gemma":0.000002541486,"domain_scores_codex":[0.9981362,0.00006829314,0.0007528473,0.000228999,0.0004260688,0.0003876113],"domain_scores_gemma":[0.9972965,0.00147185,0.0002673901,0.0002602578,0.0005762975,0.0001277586],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002564279,0.0004061167,0.0002417342,0.001250141,0.0003192017,0.00001155392,0.002536552,0.2571008,0.0007877871,0.7283281,0.001491561,0.007270082],"study_design_scores_gemma":[0.002039396,0.000242001,0.00001846499,0.0005765272,0.00008801281,0.00001667021,0.0001738248,0.8690261,0.0004418764,0.1271243,0.00008336826,0.0001694973],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9365565,0.0003005149,0.05726765,0.0001228835,0.004586878,0.00033152,0.00001074569,0.00005725023,0.0007660153],"genre_scores_gemma":[0.9877063,0.00004558825,0.01164309,0.00002648043,0.0005243029,0.000003234235,0.000007841772,0.00003739948,0.00000577626],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6119253,"threshold_uncertainty_score":0.9184386,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07095789180371882,"score_gpt":0.3157630658500992,"score_spread":0.2448051740463804,"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."}}