{"id":58,"date":"2026-07-02T22:13:12","date_gmt":"2026-07-02T20:13:12","guid":{"rendered":"https:\/\/www.calibsun.com\/?page_id=58"},"modified":"2026-07-30T15:55:15","modified_gmt":"2026-07-30T13:55:15","slug":"verificacao-de-qualidade","status":"publish","type":"page","link":"https:\/\/www.calibsun.com\/pt\/quality-check\/","title":{"rendered":"Expertise | Data Quality check"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"58\" class=\"elementor elementor-58\" data-elementor-post-type=\"page\">\n\t\t\t\t<div class=\"elementor-element elementor-element-1d084c45 e-con-full e-flex e-con e-parent\" data-id=\"1d084c45\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-14b78507 e-con-full e-flex e-con e-child\" data-id=\"14b78507\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;,&quot;background_motion_fx_motion_fx_scrolling&quot;:&quot;yes&quot;,&quot;background_motion_fx_scale_effect&quot;:&quot;yes&quot;,&quot;background_motion_fx_scale_speed&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:3,&quot;sizes&quot;:[]},&quot;background_motion_fx_scale_range&quot;:{&quot;unit&quot;:&quot;%&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:{&quot;start&quot;:50,&quot;end&quot;:100}},&quot;background_motion_fx_scale_direction&quot;:&quot;out-in&quot;,&quot;background_motion_fx_devices&quot;:[&quot;desktop&quot;,&quot;laptop&quot;,&quot;tablet_extra&quot;,&quot;tablet&quot;,&quot;mobile&quot;]}\">\n\t\t<div class=\"elementor-element elementor-element-23238e89 e-con-full e-flex e-con e-child\" data-id=\"23238e89\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-1e76f7 elementor-icon-list--layout-inline elementor-list-item-link-full_width elementor-widget elementor-widget-icon-list\" data-id=\"1e76f7\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"icon-list.default\">\n\t\t\t\t\t\t\t<ul class=\"elementor-icon-list-items elementor-inline-items\">\n\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item elementor-inline-item\">\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-icon\">\n\t\t\t\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-circle\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M256 8C119 8 8 119 8 256s111 248 248 248 248-111 248-248S393 8 256 8z\"><\/path><\/svg>\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\">Calibsun | Expertise<\/span>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t<\/ul>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1ac440fd elementor-widget elementor-widget-heading\" data-id=\"1ac440fd\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h1 class=\"elementor-heading-title elementor-size-default\"><span class=\"color-orange\">Data Quality Check<\/span> &amp; performance monitoring<\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-1d7ded27 e-flex e-con-boxed e-con e-parent\" data-id=\"1d7ded27\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-35e47a07 e-con-full e-flex e-con e-child\" data-id=\"35e47a07\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-26ef6702 e-con-full e-flex e-con e-child\" data-id=\"26ef6702\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-30631edf elementor-widget-tablet__width-initial elementor-widget elementor-widget-heading\" data-id=\"30631edf\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Why sensor quality check matters ?<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-415011bd elementor-widget-tablet__width-initial elementor-widget elementor-widget-image\" data-id=\"415011bd\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img fetchpriority=\"high\" decoding=\"async\" width=\"540\" height=\"460\" src=\"https:\/\/www.calibsun.com\/wp-content\/uploads\/2026\/07\/Export_On-site-40.jpg\" class=\"attachment-large size-large wp-image-2668\" alt=\"\" srcset=\"https:\/\/www.calibsun.com\/wp-content\/uploads\/2026\/07\/Export_On-site-40.jpg 540w, https:\/\/www.calibsun.com\/wp-content\/uploads\/2026\/07\/Export_On-site-40-300x256.jpg 300w, https:\/\/www.calibsun.com\/wp-content\/uploads\/2026\/07\/Export_On-site-40-14x12.jpg 14w\" sizes=\"(max-width: 540px) 100vw, 540px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-3c27d854 e-con-full e-flex e-con e-child\" data-id=\"3c27d854\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-51d5db0c text-pretty elementor-widget elementor-widget-text-editor\" data-id=\"51d5db0c\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">On a PV plant, data integrity directly conditions every downstream process: solar and power forecasting, performance monitoring and asset management, and solar resource assessment. A pyranometer reading affected by soiling, frost, or misalignment does not produce an obvious error signal (sensor data continues to arrive, timestamps are valid, values appear plausible). Without a structured quality check process, corrupted measurements propagate silently into machine learning algorithms and monitoring systems, degrading data quality and energy production estimates.<\/span><\/p><p><span style=\"font-weight: 400;\">CalibSun&#8217;s Sensors Expertise is designed to address this challenge across the full lifecycle of a solar PV plant: from historical data validation at onboarding, to automated real-time flagging on every incoming measurement. This data management approach ensures reliable data quality for both performance analysis and power forecasting, regardless of site size, weather conditions, or hardware configuration.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-43befca5 e-con-full e-flex e-con e-child\" data-id=\"43befca5\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-57b6e0c7 elementor-widget-tablet__width-initial elementor-widget elementor-widget-heading\" data-id=\"57b6e0c7\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Key elements<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-76a7d360 elementor-icon-list--layout-traditional elementor-list-item-link-full_width elementor-widget elementor-widget-icon-list\" data-id=\"76a7d360\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"icon-list.default\">\n\t\t\t\t\t\t\t<ul class=\"elementor-icon-list-items\">\n\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item\">\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-icon\">\n\t\t\t\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-circle\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M256 8C119 8 8 119 8 256s111 248 248 248 248-111 248-248S393 8 256 8z\"><\/path><\/svg>\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\"><b>Pyranometer misalignment, soiling, and decalibration are the primary sources of data quality issues on operational PV plants<\/b> - none produce obvious discontinuities in the time series. This technical approach ensures accurate sensor readings and protects data integrity throughout operational periods.<\/span>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item\">\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-icon\">\n\t\t\t\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-circle\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M256 8C119 8 8 119 8 256s111 248 248 248 248-111 248-248S393 8 256 8z\"><\/path><\/svg>\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\"><b>CalibSun applies a two-phase quality check:<\/b> a one-time historical data QC pass at onboarding for machine learning, and a continuous automated flagging process on every incoming measurement.<\/span>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item\">\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-icon\">\n\t\t\t\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-circle\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M256 8C119 8 8 119 8 256s111 248 248 248 248-111 248-248S393 8 256 8z\"><\/path><\/svg>\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\"><b>Meteorological cross-validation<\/b> \u2014 temperature, humidity, wind \u2014 is a critical layer for detecting invalid data and sensor anomalies, particularly under difficult winter weather conditions.<\/span>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item\">\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-icon\">\n\t\t\t\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-circle\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M256 8C119 8 8 119 8 256s111 248 248 248 248-111 248-248S393 8 256 8z\"><\/path><\/svg>\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\"><b>On utility-scale sites<\/b>, one pyranometer per 5 to 10 MW, distributed regularly across the solar power plant footprint, is required to resolve spatial irradiance data gradients.<\/span>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item\">\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-icon\">\n\t\t\t\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-circle\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M256 8C119 8 8 119 8 256s111 248 248 248 248-111 248-248S393 8 256 8z\"><\/path><\/svg>\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\"><b>Data quality directly impacts performance ratio calculations<\/b>, satellite calibration, and the reliability of machine learning forecasting models trained on historical data.<\/span>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t<\/ul>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-43dc638f e-flex e-con-boxed e-con e-parent\" data-id=\"43dc638f\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-338edccb elementor-widget elementor-widget-heading\" data-id=\"338edccb\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">What is Sensor Quality Check ?<\/h2>\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-7ff3486 e-grid e-con-full e-con e-child\" data-id=\"7ff3486\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-68e271b elementor-widget elementor-widget-text-editor\" data-id=\"68e271b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">On an operational PV plant, the pyranometer is the primary reference for incoming solar irradiance, GHI on a horizontal plane, GTI on a tilted plane matching panel orientation. It feeds performance monitoring systems, provides ground-truth irradiance data for satellite calibration, and serves as the main data acquisition input for photovoltaic power forecasting models. This active role makes sensor readings and quality inspection essential to ensure reliable data flows through every downstream application and technology platform.<\/span><\/p><p>Pyranometers are precise instruments operating in harsh outdoor conditions. Three main failure modes affect sensor performance and data quality in practice:\u00a0<\/p><p>\u00a0<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4b7c523 elementor-widget elementor-widget-text-editor\" data-id=\"4b7c523\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<ul><li aria-level=\"1\"><b>Soiling<\/b>: dust, pollen, or pollution accumulate on the sensor dome, progressively attenuating measured solar irradiance without producing obvious discontinuities.<\/li><\/ul><p>\u00a0<\/p><ul><li aria-level=\"1\"><b>Decalibration<\/b>: sensor sensitivity drifts over time, introducing systematic bias difficult to detect without a reference instrument or cross-validation.<\/li><\/ul><p>\u00a0<\/p><ul><li aria-level=\"1\"><b>Misalignment:<\/b> following incorrect installation or physical damage, the sensor no longer sits in its nominal plane, directly distorting GHI or GTI measurement readings.<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-f24d7de e-grid e-con-full e-con e-child\" data-id=\"f24d7de\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-b561760 elementor-widget elementor-widget-text-editor\" data-id=\"b561760\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">None of these failure modes are self-evident or produce missing data rates. Without data quality control, erroneous sensor data is treated as valid by forecasting models, satellite calibration pipelines, and monitoring systems alike &#8211; with direct impact on performance estimates and energy output calculations.<\/span><\/p><p><span style=\"font-weight: 400;\">Beyond irradiance sensors, meteorological sensors (measuring ambient temperature, wind speed, and relative humidity) also require data validation and quality assurance. These measurement sensors and environmental conditions data are used to cross-validate irradiance data, identify sensor anomalies, and refine photovoltaic production models. By performing rigorous testing, evaluation, and visual inspection of sensor accuracy and environmental conditions, operators ensure that sensor performance remains trustworthy across operational periods.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-f1ccc31 e-flex e-con-boxed e-con e-parent\" data-id=\"f1ccc31\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;gradient&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-3d552e8e e-con-full e-flex e-con e-child\" data-id=\"3d552e8e\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-7558627 e-con-full e-flex e-con e-child\" data-id=\"7558627\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;sticky&quot;:&quot;top&quot;,&quot;sticky_on&quot;:[&quot;desktop&quot;,&quot;laptop&quot;,&quot;tablet_extra&quot;,&quot;tablet&quot;],&quot;sticky_offset&quot;:120,&quot;sticky_parent&quot;:&quot;yes&quot;,&quot;sticky_effects_offset&quot;:0,&quot;sticky_anchor_link_offset&quot;:0}\">\n\t\t<div class=\"elementor-element elementor-element-3eecdcdd e-con-full e-flex e-con e-child\" data-id=\"3eecdcdd\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-53d908e1 elementor-icon-list--layout-inline elementor-list-item-link-full_width elementor-widget elementor-widget-icon-list\" data-id=\"53d908e1\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"icon-list.default\">\n\t\t\t\t\t\t\t<ul class=\"elementor-icon-list-items elementor-inline-items\">\n\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item elementor-inline-item\">\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-icon\">\n\t\t\t\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-circle\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M256 8C119 8 8 119 8 256s111 248 248 248 248-111 248-248S393 8 256 8z\"><\/path><\/svg>\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\">Quality check steps <\/span>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t<\/ul>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4525155 elementor-widget elementor-widget-heading\" data-id=\"4525155\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Data Quality Control Criteria: How Invalid Data Is Detected<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-3f3f7e75 e-con-full e-flex e-con e-child\" data-id=\"3f3f7e75\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-21b9109f elementor-widget elementor-widget-button\" data-id=\"21b9109f\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"#physical-consistency-erl\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Physical Consistency (ERL)<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-10069fd1 elementor-widget elementor-widget-button\" data-id=\"10069fd1\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"#meteorological-cross-validation\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Meteorological Cross-Validation<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2ab92f90 elementor-widget elementor-widget-button\" data-id=\"2ab92f90\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"#inter-sensor-spatial-consistency\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Inter-Sensor Spatial Consistency<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5d120095 elementor-widget elementor-widget-button\" data-id=\"5d120095\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"#orientation-horizontality-validation\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Orientation and Horizontality Validation<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-32432d38 e-con-full e-flex e-con e-child\" data-id=\"32432d38\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-3851e3ea e-con-full e-flex e-con e-child\" data-id=\"3851e3ea\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-5405648b elementor-absolute elementor-widget elementor-widget-menu-anchor\" data-id=\"5405648b\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;_position&quot;:&quot;absolute&quot;}\" data-widget_type=\"menu-anchor.default\">\n\t\t\t\t\t\t\t<div class=\"elementor-menu-anchor\" id=\"physical-consistency-erl\"><\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6e4367bd elementor-widget__width-inherit elementor-widget elementor-widget-image\" data-id=\"6e4367bd\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"800\" height=\"303\" src=\"https:\/\/www.calibsun.com\/wp-content\/uploads\/2026\/07\/Export_Quality-check-47-1024x388.png\" class=\"attachment-large size-large wp-image-1764\" alt=\"\" srcset=\"https:\/\/www.calibsun.com\/wp-content\/uploads\/2026\/07\/Export_Quality-check-47-1024x388.png 1024w, https:\/\/www.calibsun.com\/wp-content\/uploads\/2026\/07\/Export_Quality-check-47-300x114.png 300w, https:\/\/www.calibsun.com\/wp-content\/uploads\/2026\/07\/Export_Quality-check-47-768x291.png 768w, https:\/\/www.calibsun.com\/wp-content\/uploads\/2026\/07\/Export_Quality-check-47-18x7.png 18w, https:\/\/www.calibsun.com\/wp-content\/uploads\/2026\/07\/Export_Quality-check-47.png 1375w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3be89c30 elementor-widget elementor-widget-heading\" data-id=\"3be89c30\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Physical Consistency: Extremely Rare Limits (ERL)<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-dec76eb elementor-widget elementor-widget-text-editor\" data-id=\"dec76eb\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">The first layer of data quality verification applies physical consistency checks to the irradiance data time series. CalibSun uses the Extremely Rare Limits methodology (Long &amp; Dutton, 2002), which defines bounds based on astronomical constraints.<\/span><\/p><p><span style=\"font-weight: 400;\">Two categories of physically invalid data are flagged at this stage:<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><b>GHI values exceeding extraterrestrial irradiance:<\/b><span style=\"font-weight: 400;\"> the solar resource received above the atmosphere defines an absolute physical upper bound. Any measured value exceeding this threshold is physically impossible and is flagged unconditionally.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Negative irradiance values during daylight hours: <\/b><span style=\"font-weight: 400;\">while small negative readings can occur at night due to sensor noise, negative values during solar hours indicate a hardware fault or data processing anomaly.<\/span><\/li><\/ul><p><span style=\"font-weight: 400;\">These ERL checks form the ground layer of any rigorous data quality process, filtering out values that no statistical methods or contextual analysis could rehabilitate.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-725f44b1 e-con-full e-flex e-con e-child\" data-id=\"725f44b1\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-24211b8c elementor-absolute elementor-widget elementor-widget-menu-anchor\" data-id=\"24211b8c\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;_position&quot;:&quot;absolute&quot;}\" data-widget_type=\"menu-anchor.default\">\n\t\t\t\t\t\t\t<div class=\"elementor-menu-anchor\" id=\"meteorological-cross-validation\"><\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-66b3c487 elementor-widget__width-inherit elementor-widget elementor-widget-image\" data-id=\"66b3c487\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"800\" height=\"303\" src=\"https:\/\/www.calibsun.com\/wp-content\/uploads\/2026\/07\/Export_Quality-check-48-1024x388.png\" class=\"attachment-large size-large wp-image-1765\" alt=\"\" srcset=\"https:\/\/www.calibsun.com\/wp-content\/uploads\/2026\/07\/Export_Quality-check-48-1024x388.png 1024w, https:\/\/www.calibsun.com\/wp-content\/uploads\/2026\/07\/Export_Quality-check-48-300x114.png 300w, https:\/\/www.calibsun.com\/wp-content\/uploads\/2026\/07\/Export_Quality-check-48-768x291.png 768w, https:\/\/www.calibsun.com\/wp-content\/uploads\/2026\/07\/Export_Quality-check-48-18x7.png 18w, https:\/\/www.calibsun.com\/wp-content\/uploads\/2026\/07\/Export_Quality-check-48.png 1375w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4443dbef elementor-widget elementor-widget-heading\" data-id=\"4443dbef\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Meteorological Cross-Validation<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3f9a42a8 elementor-widget elementor-widget-text-editor\" data-id=\"3f9a42a8\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">The second layer of <strong>quality assurance exploits the physical relationships between solar irradiance and concurrent meteorological variables.<\/strong> A measurement inconsistent with observed weather conditions is a strong indicator of a sensor issue.<\/span><\/p><p><span style=\"font-weight: 400;\"><strong>The most operationally significant case involves winter conditions.<\/strong> When ambient temperature drops below 0\u00b0C and relative humidity exceeds 90%, non-heated, non-ventilated pyranometers are exposed to frost and freezing fog. The impact is systematic:<\/span><\/p><ol><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">The sensor dome frosts overnight, rendering irradiance data invalid from the first morning hours.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">As temperature rises above 0\u20132\u00b0C, the sensor gradually defrosts, but water droplets on the dome can temporarily act as a lens, producing irradiance readings that exceed the actual incoming solar resource.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Until full defrosting is confirmed, all measurements from non-heated sensors must be flagged as invalid data.<\/span><\/li><\/ol><p>\u00a0<\/p><p><span style=\"font-weight: 400;\">Heated and ventilated pyranometers are specifically designed to prevent this failure mode. In the field, this distinction has a measurable impact on data quality: during a winter campaign conducted by CalibSun at a PV site in Ardennes, France, the heated and ventilated reference pyranometer showed only 6.6% of data points excluded, while non-heated sensors required 44.7% of data points to be flagged over the same period,\u00a0 due solely to frost, freezing fog, and condensation effects.<\/span><\/p><p><span style=\"font-weight: 400;\">This field result illustrates why heated and ventilated pyranometers are the correct choice for the reference sensor in any solar energy project located in a climate with cold and humid winters.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-3adb9542 e-con-full e-flex e-con e-child\" data-id=\"3adb9542\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-58676d1 elementor-absolute elementor-widget elementor-widget-menu-anchor\" data-id=\"58676d1\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;_position&quot;:&quot;absolute&quot;}\" data-widget_type=\"menu-anchor.default\">\n\t\t\t\t\t\t\t<div class=\"elementor-menu-anchor\" id=\"inter-sensor-spatial-consistency\"><\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-40e55e23 elementor-widget__width-inherit elementor-widget elementor-widget-image\" data-id=\"40e55e23\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"800\" height=\"303\" src=\"https:\/\/www.calibsun.com\/wp-content\/uploads\/2026\/07\/Export_Quality-check-49-1024x388.png\" class=\"attachment-large size-large wp-image-1766\" alt=\"\" srcset=\"https:\/\/www.calibsun.com\/wp-content\/uploads\/2026\/07\/Export_Quality-check-49-1024x388.png 1024w, https:\/\/www.calibsun.com\/wp-content\/uploads\/2026\/07\/Export_Quality-check-49-300x114.png 300w, https:\/\/www.calibsun.com\/wp-content\/uploads\/2026\/07\/Export_Quality-check-49-768x291.png 768w, https:\/\/www.calibsun.com\/wp-content\/uploads\/2026\/07\/Export_Quality-check-49-18x7.png 18w, https:\/\/www.calibsun.com\/wp-content\/uploads\/2026\/07\/Export_Quality-check-49.png 1375w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a240943 elementor-widget elementor-widget-heading\" data-id=\"a240943\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Inter-Sensor Spatial Consistency<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-22a32d7e elementor-widget elementor-widget-text-editor\" data-id=\"22a32d7e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">On sites equipped with multiple pyranometers, data quality can be further assessed through spatial cross-validation. <strong>When two sensors at different locations on the same solar power plant show a persistent divergence that is inconsistent with expected spatial irradiance gradients, the dataset flags this as a potential sensor anomaly requiring investigation.<\/strong><\/span><\/p><p><span style=\"font-weight: 400;\">This approach also enables detection of localised shading events that affect one part of the plant but not another, a situation that a single-sensor monitoring setup would either miss or misinterpret as a global irradiance drop.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-7df44a7 e-con-full e-flex e-con e-child\" data-id=\"7df44a7\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-5068484a elementor-absolute elementor-widget elementor-widget-menu-anchor\" data-id=\"5068484a\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;_position&quot;:&quot;absolute&quot;}\" data-widget_type=\"menu-anchor.default\">\n\t\t\t\t\t\t\t<div class=\"elementor-menu-anchor\" id=\"orientation-horizontality-validation\"><\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0af6d5d elementor-widget__width-inherit elementor-widget elementor-widget-image\" data-id=\"0af6d5d\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"800\" height=\"303\" src=\"https:\/\/www.calibsun.com\/wp-content\/uploads\/2026\/07\/Export_Quality-check-50-1024x388.png\" class=\"attachment-large size-large wp-image-1767\" alt=\"\" srcset=\"https:\/\/www.calibsun.com\/wp-content\/uploads\/2026\/07\/Export_Quality-check-50-1024x388.png 1024w, https:\/\/www.calibsun.com\/wp-content\/uploads\/2026\/07\/Export_Quality-check-50-300x114.png 300w, https:\/\/www.calibsun.com\/wp-content\/uploads\/2026\/07\/Export_Quality-check-50-768x291.png 768w, https:\/\/www.calibsun.com\/wp-content\/uploads\/2026\/07\/Export_Quality-check-50-18x7.png 18w, https:\/\/www.calibsun.com\/wp-content\/uploads\/2026\/07\/Export_Quality-check-50.png 1375w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-60c8d43 elementor-widget elementor-widget-heading\" data-id=\"60c8d43\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Orientation and Horizontality Validation<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-530cf30 elementor-widget elementor-widget-text-editor\" data-id=\"530cf30\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\"><strong>Misalignment of a pyranometer, whether due to incorrect installation or mechanical drift, directly distorts GHI and GTI measurements.<\/strong> CalibSun validates sensor orientation by comparing measured irradiance data against the theoretical clear-sky irradiance profile on cloud-free days. Under clear-sky conditions, a correctly oriented and horizontal pyranometer must track the expected clear-sky envelope with high precision in both amplitude and temporal shape.<\/span><\/p><p><span style=\"font-weight: 400;\">Any systematic deviation, a consistent offset, an asymmetry between morning and afternoon readings, or a peak occurring earlier or later than the theoretical maximum,\u00a0 indicates an orientation issue that requires field correction. This validation step is implemented as part of every sensor audit and at the start of all services : during satellite calibration campaigns and when implementing forecasting algorithms.<strong> If the quality check detects an issue on one of the on-site sensors, our expert team can provide direct recommendations to the operators<\/strong> to help them resolve it and improve forecast accuracy. <\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-822796d e-flex e-con-boxed e-con e-parent\" data-id=\"822796d\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-a2e7051 elementor-widget elementor-widget-heading\" data-id=\"a2e7051\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Historical and Real-Time Quality Check<\/h2>\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-a95ee4c e-grid e-con-full e-con e-child\" data-id=\"a95ee4c\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-e16bf56 e-con-full e-flex e-con e-child\" data-id=\"e16bf56\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-4482319 elementor-widget elementor-widget-heading\" data-id=\"4482319\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Historical Data Quality Control<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5f76584 elementor-widget elementor-widget-text-editor\" data-id=\"5f76584\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">When CalibSun onboards a new solar PV plant, the first step is a <\/span><b>systematic data quality control pass over the full ava<\/b><span style=\"font-weight: 400;\">ilable historical data archive. This process typically covers one to several years of time series from <\/span><b>SCADA systems, pyranometers, and meteorological sensors<\/b><span style=\"font-weight: 400;\">.\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">The objective is to produce a clean, validated dataset that can serve as the training base for the machine learning forecasting model. E<\/span><b>very flagged or missing data point is documented<\/b><span style=\"font-weight: 400;\">. Missing values are not filled arbitrarily &#8211; gaps in the historical data are either left explicitly marked or reconstructed using physically consistent methods where the gap duration and surrounding context allow it.<\/span><\/p><p><span style=\"font-weight: 400;\">The quality of this<\/span><b> initial data processing step has a direct and lasting impact on forecast accuracy<\/b><span style=\"font-weight: 400;\">: a model trained on corrupted or biased historical data will encode systematic errors that persist throughout its operational lifetime. Accurate <\/span><b>data collection and rigorous data validation at this stage are therefore not optional<\/b><span style=\"font-weight: 400;\">, they are the foundation of performance and reliability for every subsequent forecast.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-fe191ed e-con-full e-flex e-con e-child\" data-id=\"fe191ed\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-f87283f elementor-widget elementor-widget-heading\" data-id=\"f87283f\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Automated Real-Time Data Quality Control<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f4694f1 elementor-widget elementor-widget-text-editor\" data-id=\"f4694f1\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Once a solar power plant is operational within CalibSun&#8217;s forecasting pipeline, sensor data is ingested continuously via API at the highest available resolution,\u00a0 optimally 1 minute, operationally 5 to 15 minutes depending on SCADA configuration.<\/span><\/p><p><span style=\"font-weight: 400;\">Every incoming measurement passes through the automated quality check pipeline before being used in the forecasting process. This pipeline applies, in sequence:<\/span><\/p><ol><li style=\"font-weight: 400;\" aria-level=\"1\"><b>ERL physical consistency checks<\/b><span style=\"font-weight: 400;\">,\u00a0 filtering physically <\/span><b>invalid data<\/b><span style=\"font-weight: 400;\"> unconditionally.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Meteorological cross-validation<\/b><span style=\"font-weight: 400;\">,\u00a0 best practices for flagging readings inconsistent with concurrent <\/span><b>temperature<\/b><span style=\"font-weight: 400;\">, humidity, and wind <\/span><b>parameters<\/b><span style=\"font-weight: 400;\">.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Inter-sensor consistency checks<\/b><span style=\"font-weight: 400;\">, comparing simultaneous readings across distributed pyranometers where available.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Clear-sky envelope verification<\/b><span style=\"font-weight: 400;\">, detecting values that exceed the theoretical maximum <\/span><b>solar irradiance<\/b><span style=\"font-weight: 400;\"> for the current solar geometry and atmospheric conditions.<\/span><\/li><\/ol><p><span style=\"font-weight: 400;\">\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">When a measurement is flagged, the system does not simply discard it. <strong>The flagging logic records the reason, the time of occurrence, and the associated meteorological context.<\/strong> This structured approach to data management enables retrospective analysis of sensor behaviour over time, supports maintenance scheduling, and feeds back into the calibration of quality thresholds.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-761d715 e-con-full e-flex e-con e-child\" data-id=\"761d715\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-e6b414b elementor-widget elementor-widget-text-editor\" data-id=\"e6b414b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>Alerts can be triggered when a sensor produces an abnormal rate of flagged values over a defined time window,\u00a0 providing an early alert signal before a hardware failure fully compromises the dataset.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-f707ab9 e-flex e-con-boxed e-con e-parent\" data-id=\"f707ab9\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;gradient&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-5d0d5e6 e-con-full e-flex e-con e-child\" data-id=\"5d0d5e6\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-708c98b elementor-widget__width-initial elementor-widget elementor-widget-image\" data-id=\"708c98b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"745\" height=\"635\" src=\"https:\/\/www.calibsun.com\/wp-content\/uploads\/2026\/07\/Export_Weather-data-52.jpg\" class=\"attachment-large size-large wp-image-2685\" alt=\"\" srcset=\"https:\/\/www.calibsun.com\/wp-content\/uploads\/2026\/07\/Export_Weather-data-52.jpg 745w, https:\/\/www.calibsun.com\/wp-content\/uploads\/2026\/07\/Export_Weather-data-52-300x256.jpg 300w, https:\/\/www.calibsun.com\/wp-content\/uploads\/2026\/07\/Export_Weather-data-52-14x12.jpg 14w\" sizes=\"(max-width: 745px) 100vw, 745px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-bc988ca e-con-full e-flex e-con e-child\" data-id=\"bc988ca\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-4e2fb80 elementor-widget elementor-widget-heading\" data-id=\"4e2fb80\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\"><span class=\"color-orange\">Sensor Quality Check<\/span> and Its Impact on Forecasting Performance<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6c86dc4 elementor-widget elementor-widget-text-editor\" data-id=\"6c86dc4\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>Data quality issues at the sensor level have cascading effects on every layer of the forecasting process.<\/p><p>Rigorous sensor quality check is therefore not a standalone engineering task. <strong>It is structurally integrated into forecasting accuracy, satellite calibration quality, performance monitoring reliability, and energy trading risk exposure.<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-cb6d976 e-grid e-con-full e-con e-child\" data-id=\"cb6d976\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-08ea7c4 e-con-full e-flex e-con e-child\" data-id=\"08ea7c4\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-5a234fc elementor-widget elementor-widget-heading\" data-id=\"5a234fc\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Satellite calibration<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-20c3fcd elementor-widget elementor-widget-text-editor\" data-id=\"20c3fcd\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">CalibSun uses local ground-truth irradiance data to calibrate satellite-derived irradiance products. If the reference pyranometer carries a systematic bias, the satellite calibration absorbs that bias,\u00a0 degrading the spatial representativeness of the corrected irradiance field across the entire plant footprint.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-ffe394c e-con-full e-flex e-con e-child\" data-id=\"ffe394c\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-d30818f elementor-widget elementor-widget-heading\" data-id=\"d30818f\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Model training<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6128972 elementor-widget elementor-widget-text-editor\" data-id=\"6128972\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Machine learning models trained on historical data containing undetected data quality issues will encode those errors as signal. The resulting model will generalise poorly to clean input conditions, producing forecasts with degraded accuracy and poor performance metrics.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-d916420 e-con-full e-flex e-con e-child\" data-id=\"d916420\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-9b53b00 elementor-widget elementor-widget-heading\" data-id=\"9b53b00\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Real-time recalibration<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-154c0ee elementor-widget elementor-widget-text-editor\" data-id=\"154c0ee\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">CalibSun&#8217;s NEXT algorithm recalibrates continuously using the most recent local sensor data. If incoming measurements are corrupted and pass undetected through the quality check, the recalibration step will adjust the model in the wrong direction, temporarily increasing forecast error rather than reducing it.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-237cbc9 e-con-full e-flex e-con e-child\" data-id=\"237cbc9\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-20194cc elementor-widget elementor-widget-heading\" data-id=\"20194cc\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Performance ratio and asset management<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6dbcb91 elementor-widget elementor-widget-text-editor\" data-id=\"6dbcb91\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Performance monitoring relies on accurate irradiance data to compute the performance ratio and detect underperforming strings or inverters. Biased irradiance inputs produce biased performance ratio values, masking real degradation or generating false alerts,\u00a0 both of which carry direct asset management consequences.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-2dc5254f e-con-full e-flex e-con e-parent\" data-id=\"2dc5254f\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;gradient&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-5d943aec elementor-widget__width-initial elementor-widget elementor-widget-image\" data-id=\"5d943aec\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"683\" height=\"1024\" src=\"https:\/\/www.calibsun.com\/wp-content\/uploads\/2026\/07\/Sensor-expertise-FAQ-683x1024.jpg\" class=\"attachment-large size-large wp-image-2992\" alt=\"\" srcset=\"https:\/\/www.calibsun.com\/wp-content\/uploads\/2026\/07\/Sensor-expertise-FAQ-683x1024.jpg 683w, https:\/\/www.calibsun.com\/wp-content\/uploads\/2026\/07\/Sensor-expertise-FAQ-200x300.jpg 200w, https:\/\/www.calibsun.com\/wp-content\/uploads\/2026\/07\/Sensor-expertise-FAQ-768x1152.jpg 768w, https:\/\/www.calibsun.com\/wp-content\/uploads\/2026\/07\/Sensor-expertise-FAQ-8x12.jpg 8w, https:\/\/www.calibsun.com\/wp-content\/uploads\/2026\/07\/Sensor-expertise-FAQ.jpg 900w\" sizes=\"(max-width: 683px) 100vw, 683px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-bd87dc8 e-con-full e-flex e-con e-child\" data-id=\"bd87dc8\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-4e132491 e-con-full e-flex e-con e-child\" data-id=\"4e132491\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-12124faf elementor-icon-list--layout-inline elementor-list-item-link-full_width elementor-widget elementor-widget-icon-list\" data-id=\"12124faf\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"icon-list.default\">\n\t\t\t\t\t\t\t<ul class=\"elementor-icon-list-items elementor-inline-items\">\n\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item elementor-inline-item\">\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-icon\">\n\t\t\t\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-circle\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M256 8C119 8 8 119 8 256s111 248 248 248 248-111 248-248S393 8 256 8z\"><\/path><\/svg>\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\">Questions<\/span>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t<\/ul>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4c2d5f56 elementor-widget elementor-widget-heading\" data-id=\"4c2d5f56\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Frequently asked questions about Sensor Quality Check<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4addbc16 elementor-widget elementor-widget-n-accordion\" data-id=\"4addbc16\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;default_state&quot;:&quot;all_collapsed&quot;,&quot;max_items_expended&quot;:&quot;one&quot;,&quot;n_accordion_animation_duration&quot;:{&quot;unit&quot;:&quot;ms&quot;,&quot;size&quot;:400,&quot;sizes&quot;:[]}}\" data-widget_type=\"nested-accordion.default\">\n\t\t\t\t\t\t\t<div class=\"e-n-accordion\" aria-label=\"Accordion. Open links with Enter or Space, close with Escape, and navigate with Arrow Keys\">\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-1250\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"1\" tabindex=\"0\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-1250\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><h3 class=\"e-n-accordion-item-title-text\"> How many pyranometers are needed on a utility-scale solar plant? <\/h3><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"40\" height=\"40\" viewBox=\"0 0 40 40\" fill=\"none\"><path d=\"M24.6998 11.6351L31.5103 27.0167C32.1857 28.5487 33.3677 30.1421 35 31L33.1989 31C30.3846 31 27.8518 29.2228 26.6135 26.4652L20.8724 13.1671C20.8724 13.1671 20.591 12.6769 20.0844 12.7382C19.5779 12.7382 19.2964 13.1671 19.2964 13.1671L13.5553 26.4652C12.3734 29.2228 9.78424 31 6.96998 31L5 31C6.63227 30.1421 7.81426 28.5487 8.48968 27.0167L15.3002 11.6351C16.0882 9.61282 18.7899 9 19.9719 9C21.1538 9 23.8555 9.55153 24.6435 11.6351L24.6998 11.6351Z\" fill=\"currentColor\"><\/path><\/svg><\/span>\n\t\t\t<span class='e-closed'><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"40\" height=\"40\" viewBox=\"0 0 40 40\" fill=\"none\"><path d=\"M24.6998 28.3649L31.5103 12.9833C32.1857 11.4513 33.3677 9.85794 35 9L33.1989 9C30.3846 9 27.8518 10.7772 26.6135 13.5348L20.8724 26.8329C20.8724 26.8329 20.591 27.3231 20.0844 27.2618C19.5779 27.2618 19.2964 26.8329 19.2964 26.8329L13.5553 13.5348C12.3734 10.7772 9.78424 9 6.96998 9L5 9C6.63227 9.85794 7.81426 11.4513 8.48968 12.9833L15.3002 28.3649C16.0882 30.3872 18.7899 31 19.9719 31C21.1538 31 23.8555 30.4485 24.6435 28.3649L24.6998 28.3649Z\" fill=\"currentColor\"><\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-1250\" class=\"elementor-element elementor-element-7be4b2ec e-con-full e-flex e-con e-child\" data-id=\"7be4b2ec\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-139c870a elementor-widget elementor-widget-text-editor\" data-id=\"139c870a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">For small <\/span><b>PV plants<\/b><span style=\"font-weight: 400;\">, a single quality pyranometer is generally sufficient. For <\/span><b>utility-scale<\/b><span style=\"font-weight: 400;\"> sites, cloud cover moves across the <\/span><b>plant<\/b><span style=\"font-weight: 400;\"> footprint at varying speeds, creating spatial <\/span><b>irradiance<\/b><span style=\"font-weight: 400;\"> gradients that a single sensor cannot resolve. CalibSun recommends one pyranometer per 5 to 10 MW, distributed regularly across the site, to enable spatially resolved <\/span><b>irradiance data<\/b> <b>analysis<\/b><span style=\"font-weight: 400;\"> and robust <\/span><b>data quality<\/b><span style=\"font-weight: 400;\"> cross-validation. This standard design follows recommendation from <\/span><span style=\"font-weight: 400;\">Task 16 from IEA PVPS <\/span><span style=\"font-weight: 400;\">(<\/span><span style=\"font-weight: 400;\">Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications: Fourth Edition \/ Manajit Sengupta, Aron Habte, Stefan Wilbert, Christian Gueymard, Jan Remund, Elke Lorenz, Wilfried van Sark, and Adam R. Jensen &#8211; 10.69766\/ENEH5295 978-3-907281-66-6<\/span><span style=\"font-weight: 400;\"> )\u00a0 ensures product quality and accurate measurement across the supply chain.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-1251\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"2\" tabindex=\"-1\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-1251\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><h3 class=\"e-n-accordion-item-title-text\"> What happens when sensor data is flagged or missing? <\/h3><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"40\" height=\"40\" viewBox=\"0 0 40 40\" fill=\"none\"><path d=\"M24.6998 11.6351L31.5103 27.0167C32.1857 28.5487 33.3677 30.1421 35 31L33.1989 31C30.3846 31 27.8518 29.2228 26.6135 26.4652L20.8724 13.1671C20.8724 13.1671 20.591 12.6769 20.0844 12.7382C19.5779 12.7382 19.2964 13.1671 19.2964 13.1671L13.5553 26.4652C12.3734 29.2228 9.78424 31 6.96998 31L5 31C6.63227 30.1421 7.81426 28.5487 8.48968 27.0167L15.3002 11.6351C16.0882 9.61282 18.7899 9 19.9719 9C21.1538 9 23.8555 9.55153 24.6435 11.6351L24.6998 11.6351Z\" fill=\"currentColor\"><\/path><\/svg><\/span>\n\t\t\t<span class='e-closed'><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"40\" height=\"40\" viewBox=\"0 0 40 40\" fill=\"none\"><path d=\"M24.6998 28.3649L31.5103 12.9833C32.1857 11.4513 33.3677 9.85794 35 9L33.1989 9C30.3846 9 27.8518 10.7772 26.6135 13.5348L20.8724 26.8329C20.8724 26.8329 20.591 27.3231 20.0844 27.2618C19.5779 27.2618 19.2964 26.8329 19.2964 26.8329L13.5553 13.5348C12.3734 10.7772 9.78424 9 6.96998 9L5 9C6.63227 9.85794 7.81426 11.4513 8.48968 12.9833L15.3002 28.3649C16.0882 30.3872 18.7899 31 19.9719 31C21.1538 31 23.8555 30.4485 24.6435 28.3649L24.6998 28.3649Z\" fill=\"currentColor\"><\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-1251\" class=\"elementor-element elementor-element-1d6e6048 e-con-full e-flex e-con e-child\" data-id=\"1d6e6048\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-f422745 elementor-widget elementor-widget-text-editor\" data-id=\"f422745\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">When sensor data is flagged as invalid or temporarily unavailable, the forecasting system continues to operate using satellite and NWP inputs alone. Forecast accuracy is reduced during these gaps, but the service does not interrupt. As valid measurements resume, the model recalibrates automatically. Persistent missing data or high flagging rates trigger an alert for field investigation &#8211; enabling you to identify and diagnose potential sensor issues before they propagate into the forecasting dataset.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-1252\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"3\" tabindex=\"-1\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-1252\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><h3 class=\"e-n-accordion-item-title-text\"> How does sensor data quality affect the performance ratio? <\/h3><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"40\" height=\"40\" viewBox=\"0 0 40 40\" fill=\"none\"><path d=\"M24.6998 11.6351L31.5103 27.0167C32.1857 28.5487 33.3677 30.1421 35 31L33.1989 31C30.3846 31 27.8518 29.2228 26.6135 26.4652L20.8724 13.1671C20.8724 13.1671 20.591 12.6769 20.0844 12.7382C19.5779 12.7382 19.2964 13.1671 19.2964 13.1671L13.5553 26.4652C12.3734 29.2228 9.78424 31 6.96998 31L5 31C6.63227 30.1421 7.81426 28.5487 8.48968 27.0167L15.3002 11.6351C16.0882 9.61282 18.7899 9 19.9719 9C21.1538 9 23.8555 9.55153 24.6435 11.6351L24.6998 11.6351Z\" fill=\"currentColor\"><\/path><\/svg><\/span>\n\t\t\t<span class='e-closed'><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"40\" height=\"40\" viewBox=\"0 0 40 40\" fill=\"none\"><path d=\"M24.6998 28.3649L31.5103 12.9833C32.1857 11.4513 33.3677 9.85794 35 9L33.1989 9C30.3846 9 27.8518 10.7772 26.6135 13.5348L20.8724 26.8329C20.8724 26.8329 20.591 27.3231 20.0844 27.2618C19.5779 27.2618 19.2964 26.8329 19.2964 26.8329L13.5553 13.5348C12.3734 10.7772 9.78424 9 6.96998 9L5 9C6.63227 9.85794 7.81426 11.4513 8.48968 12.9833L15.3002 28.3649C16.0882 30.3872 18.7899 31 19.9719 31C21.1538 31 23.8555 30.4485 24.6435 28.3649L24.6998 28.3649Z\" fill=\"currentColor\"><\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-1252\" class=\"elementor-element elementor-element-5c14958c e-con-full e-flex e-con e-child\" data-id=\"5c14958c\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-6e0cfe2 elementor-widget elementor-widget-text-editor\" data-id=\"6e0cfe2\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">The performance ratio is computed as actual energy output divided by expected energy output based on measured irradiance data. If the pyranometer carries a systematic bias \u2014 whether from soiling, decalibration, or misalignment \u2014 the expected energy output reference is incorrect. This produces a biased performance ratio that either masks real degradation or generates false alerts \u2014 both of which have direct asset management and O&amp;M impact. <\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-1253\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"4\" tabindex=\"-1\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-1253\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><h3 class=\"e-n-accordion-item-title-text\"> Can data quality issues be detected automatically? <\/h3><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"40\" height=\"40\" viewBox=\"0 0 40 40\" fill=\"none\"><path d=\"M24.6998 11.6351L31.5103 27.0167C32.1857 28.5487 33.3677 30.1421 35 31L33.1989 31C30.3846 31 27.8518 29.2228 26.6135 26.4652L20.8724 13.1671C20.8724 13.1671 20.591 12.6769 20.0844 12.7382C19.5779 12.7382 19.2964 13.1671 19.2964 13.1671L13.5553 26.4652C12.3734 29.2228 9.78424 31 6.96998 31L5 31C6.63227 30.1421 7.81426 28.5487 8.48968 27.0167L15.3002 11.6351C16.0882 9.61282 18.7899 9 19.9719 9C21.1538 9 23.8555 9.55153 24.6435 11.6351L24.6998 11.6351Z\" fill=\"currentColor\"><\/path><\/svg><\/span>\n\t\t\t<span class='e-closed'><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"40\" height=\"40\" viewBox=\"0 0 40 40\" fill=\"none\"><path d=\"M24.6998 28.3649L31.5103 12.9833C32.1857 11.4513 33.3677 9.85794 35 9L33.1989 9C30.3846 9 27.8518 10.7772 26.6135 13.5348L20.8724 26.8329C20.8724 26.8329 20.591 27.3231 20.0844 27.2618C19.5779 27.2618 19.2964 26.8329 19.2964 26.8329L13.5553 13.5348C12.3734 10.7772 9.78424 9 6.96998 9L5 9C6.63227 9.85794 7.81426 11.4513 8.48968 12.9833L15.3002 28.3649C16.0882 30.3872 18.7899 31 19.9719 31C21.1538 31 23.8555 30.4485 24.6435 28.3649L24.6998 28.3649Z\" fill=\"currentColor\"><\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-1253\" class=\"elementor-element elementor-element-4c5267e9 e-con-full e-flex e-con e-child\" data-id=\"4c5267e9\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-5e1122ea elementor-widget elementor-widget-text-editor\" data-id=\"5e1122ea\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Yes. CalibSun&#8217;s automated quality check pipeline applies physical consistency checks, meteorological cross-validation, inter-sensor spatial consistency analysis, and clear-sky envelope verification to every incoming measurement in real time. When a sensor produces an abnormal rate of flagged values over a defined time window, an alert is triggered for field investigation before the issue propagates into the forecasting dataset. <\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t<script type=\"application\/ld+json\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@type\":\"FAQPage\",\"mainEntity\":[{\"@type\":\"Question\",\"name\":\"How many pyranometers are needed on a utility-scale solar plant?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"For small PV plants, a single quality pyranometer is generally sufficient. For utility-scale sites, cloud cover moves across the plant footprint at varying speeds, creating spatial irradiance gradients that a single sensor cannot resolve. CalibSun recommends one pyranometer per 5 to 10 MW, distributed regularly across the site, to enable spatially resolved irradiance data analysis and robust data quality cross-validation. This standard design follows recommendation from Task 16 from IEA PVPS (Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications: Fourth Edition \\\/ Manajit Sengupta, Aron Habte, Stefan Wilbert, Christian Gueymard, Jan Remund, Elke Lorenz, Wilfried van Sark, and Adam R. Jensen &#8211; 10.69766\\\/ENEH5295 978-3-907281-66-6 )\\u00a0 ensures product quality and accurate measurement across the supply chain.\"}},{\"@type\":\"Question\",\"name\":\"What happens when sensor data is flagged or missing?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"When sensor data is flagged as invalid or temporarily unavailable, the forecasting system continues to operate using satellite and NWP inputs alone. Forecast accuracy is reduced during these gaps, but the service does not interrupt. As valid measurements resume, the model recalibrates automatically. 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CalibSun&#8217;s automated quality check pipeline applies physical consistency checks, meteorological cross-validation, inter-sensor spatial consistency analysis, and clear-sky envelope verification to every incoming measurement in real time. When a sensor produces an abnormal rate of flagged values over a defined time window, an alert is triggered for field investigation before the issue propagates into the forecasting dataset.\"}}]}<\/script>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Calibsun | Expertise Data Quality Check &amp; performance monitoring Why sensor quality check matters ? On a PV plant, data integrity directly conditions every downstream process: solar and power forecasting, performance monitoring and asset management, and solar resource assessment. A pyranometer reading affected by soiling, frost, or misalignment does not produce an obvious error signal [&hellip;]<\/p>\n","protected":false},"author":4,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"class_list":["post-58","page","type-page","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.calibsun.com\/pt\/wp-json\/wp\/v2\/pages\/58","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.calibsun.com\/pt\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.calibsun.com\/pt\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.calibsun.com\/pt\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/www.calibsun.com\/pt\/wp-json\/wp\/v2\/comments?post=58"}],"version-history":[{"count":0,"href":"https:\/\/www.calibsun.com\/pt\/wp-json\/wp\/v2\/pages\/58\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.calibsun.com\/pt\/wp-json\/wp\/v2\/media?parent=58"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}