{"id":"W2186560469","doi":"10.1007/978-3-319-07455-9_20","title":"Developing Data-driven Models to Predict BEMS Energy Consumption for Demand Response Systems","year":2014,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Computer science; Energy consumption; Building management system; Smart grid; Key (lock); Energy modeling; Chiller; Data modeling; Demand response; Building automation; Energy (signal processing); Consumption (sociology); Energy management; Database; Artificial intelligence; Engineering; Electricity; Computer security; Control (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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006122452,0.0007977057,0.0006644311,0.0003857327,0.0003075633,0.001009521,0.001076949,0.000932936,0.002631677],"category_scores_gemma":[0.002531139,0.0008878172,0.0008448359,0.0004588753,0.0001732675,0.0009772452,0.0004721435,0.001699157,0.0009209439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009526155,"about_ca_system_score_gemma":0.001041143,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0206385,"about_ca_topic_score_gemma":0.0233476,"domain_scores_codex":[0.9998454,0.00003878006,0.00001083378,0.00003735714,0.000045586,0.0000220917],"domain_scores_gemma":[0.9990835,0.000608517,0.00003990634,0.00004895058,0.0001925357,0.00002648166],"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":[0.00002402512,0.00003502939,0.0004612475,0.00002539735,0.00002245536,0.00001544559,0.00000922235,0.9834549,0.0005211777,0.0008018679,0.0008050819,0.01382417],"study_design_scores_gemma":[0.000001437644,0.000002897779,0.0000456767,0.000001321155,0.000001644061,0.000001168426,0.000001532756,0.9991707,0.0001848892,0.0004493954,0.0001382562,0.000001221155],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09940086,0.000706682,0.8865711,0.000748607,0.0002249317,0.000172321,0.001643424,0.003406441,0.007125666],"genre_scores_gemma":[0.8168156,0.0005140727,0.1715982,0.0001877917,0.00009090787,0.0003699917,0.002791262,0.0003125654,0.007319645],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0206385,"threshold_uncertainty_score":0.04103673,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05269484181447198,"score_gpt":0.2520939832018173,"score_spread":0.1993991413873453,"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."}}