SolarAnywhere

SolarAnywhere

EPIC Solar Forecasting Task 2 Final Report: Data Forecasting Accuracy

This report, prepared with an Electric Program Investment Charge (EPIC) fund, describes the results of research to...

Advancing the Science of Behind-the-Meter PV Forecasting

This presentation discusses how probabilistic forecasting enables electric utilities and grid operators to reduce...

Extending Fleet Forecasting Capability into the Probabilistic Realm

This presentation demonstrates how a new approach to PV fleet forecasting can address the problem of artificially high...

Satellite-to-Irradiance Modeling – A New Version of the SUNY Model

This article presents and validates the latest version of the SUNY model for using satellite imagery to calculate...

Behind-the-Meter PV Fleet Forecasting

Grid-connected PV in the U.S. has grown substantially over the past several years and grid operators are increasingly...

Forecasting Output for 130,000 PV Systems in California

Lean how SolarAnywhere® FleetView™ is being integrated into CAISO planning and operations tools to provide power...

Predicting Short-term Variability of High Penetration PV

This article evaluates the ability of three operational satellite models (SolarAnywhere® Standard, Enhanced, and High...

Determining Storage Reserves for Regulating Solar Variability

This paper describes the initial validation of a method of quantifying PV variability by using satellite-derived solar...

Blog

Reduce uncertainty with higher-resolution typical year solar resource data
Reduce uncertainty with higher-resolution typical year solar resource data

Typical year irradiance datasets (TGY, TDY) summarize solar resource at a location. This data is generated to reflect the most probable (P50) monthly total irradiance, based on a long history of solar resource. SolarAnywhere calculates typical year data from a maximum span of time-series observations for each region—more than twenty-five years in many areas. Our […]

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