Research: Solar Prediction

Research: Solar Prediction

Observed Recent Trends in the Solar Resource across North America: Changing Cloud-cover, AOD and the Implications for PV Yield

In years recent to 2020, low solar-resource in the Eastern U.S. was observed in the SolarAnywhere® dataset. This paper...

Improved PV System Modeling with ML-Based Power Model: Case Study of a Commercial Building

Solar PV system specifications are critical to accurately modeling PV system production (along with high-quality...

EPIC Solar Forecasting Task 3 Final Report: Grid-Connected and Embedded PV Fleet Forecasting Accuracy

This report, prepared with an Electric Program Investment Charge (EPIC) fund, describes new methods for improving...

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...

Day-Ahead Irradiance Forecast Variability Characterization Using Satellite Data

This article calculates day-head forecast variability as function of historical clearness index based on intraday...

Importance of Input Data and Uncertainty Associated with Tuning Satellite to Ground Solar Irradiation

Although the uncertainty of satellite data such as SolarAnywhere® Data has been shown to be low, it can be challenging...

Solar Energy Forecast Validation for Extended Areas & Economic Impact of Forecast Accuracy

This article evaluates the accuracy of solar forecast models, including SolarAnywhere®, as a function of geographic...

Integration of Behind-the-Meter PV Fleet Forecasts into Utility Grid System Operations

This paper describes a study, funded by the U.S. Department of Energy, to evaluate new, state-of-the-art solar...

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...

Reporting of Irradiance Model Relative Errors

Metrics used in assessing irradiance model accuracy such as Root Mean Square Error (RMSE) and Mean Absolute Error...

Off-Shore Wind and Grid-Connected PV: High Penetration Peak Shaving for New York City

This article presents an experimental evaluation of the combined effective capacity of off-shore wind and...

Evaluating Irradiance Accuracy Using California ISO Data: Lessons Learned

This presentation provides the results of a study evaluating the short, medium and long-term accuracy of irradiance...

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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