{"id":"W2527543035","doi":"10.1007/978-3-319-47096-2_24","title":"Predicting the Electricity Consumption of Buildings: An Improved CBR Approach","year":2016,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Computer science; Electricity; Similarity (geometry); Case-based reasoning; Consumption (sociology); Adaptation (eye); Energy consumption; Machine learning; Artificial intelligence; Data mining; Engineering","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":[0.0009019222,0.001113304,0.001459407,0.00222902,0.0003325989,0.001319908,0.002711538,0.001340852,0.003127525],"category_scores_gemma":[0.003249474,0.0003898597,0.001068975,0.002341424,0.000312228,0.001149441,0.0006358184,0.001104585,0.0009954191],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006827416,"about_ca_system_score_gemma":0.0005915446,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01611686,"about_ca_topic_score_gemma":0.009481857,"domain_scores_codex":[0.9992383,0.0001728581,0.00006074596,0.0001875075,0.0002625588,0.00007816154],"domain_scores_gemma":[0.9986644,0.0008219901,0.0000583502,0.0001152643,0.0003097494,0.00003030982],"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":[0.000230822,0.0004449656,0.002544138,0.000284795,0.0001814341,0.0004114351,0.00009404115,0.4957654,0.007119626,0.002679067,0.003576395,0.4866679],"study_design_scores_gemma":[0.00001877851,0.00003431306,0.0005258386,0.00001261509,0.00006707344,0.00005908225,0.0000176008,0.9956815,0.001078084,0.001827693,0.0006649815,0.00001238806],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0771229,0.002596642,0.9006262,0.0005671151,0.0003223144,0.0002873005,0.0008161101,0.003581462,0.01408009],"genre_scores_gemma":[0.694476,0.001538682,0.2958089,0.0003844941,0.0002901088,0.0001657456,0.0009646225,0.0001475518,0.006224019],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01611686,"threshold_uncertainty_score":0.03204608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01416715414972372,"score_gpt":0.2133577643842182,"score_spread":0.1991906102344945,"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."}}