{"id":"W4400687138","doi":"10.20944/preprints202407.1128.v1","title":"Remote Sensing-Based Estimation of Rooftop Photovoltaic Power Production Using Physical Conversion Models and Weather Data","year":2024,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Solar Radiation and Photovoltaics","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Impact","funders":"","keywords":"Photovoltaic system; Environmental science; Observability; Meteorology; Grid-connected photovoltaic power system; Electricity; Electric power; Grid; Electricity generation; Production (economics); Solar irradiance; Irradiance; Nameplate capacity; Building-integrated photovoltaics; Computer science; Automotive engineering; Remote sensing; Power (physics); Maximum power point tracking; Engineering; Electrical engineering; Voltage; Geography","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":[0.0003178783,0.0006345888,0.0003488662,0.0004608303,0.0001423279,0.0005378336,0.000305461,0.0005194111,0.0004787222],"category_scores_gemma":[0.001317674,0.0003046083,0.000492698,0.0004827717,0.0001868688,0.0007457685,0.0003259795,0.0003857196,0.0002685133],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00029561,"about_ca_system_score_gemma":0.0002643192,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007881124,"about_ca_topic_score_gemma":0.007563531,"domain_scores_codex":[0.9998115,0.00004388081,0.000007192752,0.00008045117,0.00003459324,0.00002248579],"domain_scores_gemma":[0.9996303,0.0001995558,0.00005459773,0.00005872869,0.00004080494,0.00001597984],"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":[0.0001363826,0.0001696302,0.02345899,0.00006136104,0.0000957498,0.0001244625,0.00004254491,0.9134316,0.01627832,0.0004726226,0.0004501636,0.04527813],"study_design_scores_gemma":[0.000004675872,0.00001343432,0.008817602,0.000002211525,0.00000686264,0.00001368709,0.000008122925,0.9895091,0.001329339,0.000211998,0.00007797826,0.000004981667],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9028943,0.000305057,0.09320492,0.0001023152,0.00002497084,0.00002692683,0.0006441801,0.0008133576,0.00198388],"genre_scores_gemma":[0.98427,0.0001035619,0.01456613,0.00001062142,0.00001393157,0.00001074268,0.000701245,0.000018293,0.0003053917],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007881124,"threshold_uncertainty_score":0.01567054,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1530949651684962,"score_gpt":0.3540380652236164,"score_spread":0.2009431000551202,"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."}}