High-Fidelity Solar Power Forecasting Systems for the 392 MW Ivanpah Solar Plant (CSP) and the 250 MW California Valley Solar Ranch (PV)

Improving forecasting methods at the Ivanpah Solar Thermal plant

The Regents of the University of California, San Diego


La Jolla, CA

Recipient Location


Senate District


Assembly District



Amount Spent



Project Status

Project Result

The project demonstrated that the forecasting accuracy for DNI and POA irradiance across all time-scales (intra-hour, intra-day, day-ahead) can be improved using forecasting models that blend local and remote telemetry. The project results demonstrated the importance of having a rich set of input data to improve forecasting. This conclusion was more pronounced for the intra-hour time scale, in which multiple solar sensor data -- including sky images and high-resolution satellite images -- are essential to reduce forecasting errors. The tools developed were used in two very distinct testbeds (Ivanpah and CVSR) to predict irradiance and power generation. In both cases, the forecasting accuracy was improved relative to baseline models. The models were successfully applied to other locations, showing that they can be used in other grid-connected solar farms.

The Issue

Accurate forecasting tools for solar irradiance and solar power output have the potential to increase the reliability of California's energy supply, and the ability to optimize the dispatch of energy sources by reducing the uncertainty created by fast-changing weather conditions. High fidelity solar forecasting is an enabling technology for increasing solar penetration into the grid. However, there is a lack of well-developed forecasting models for components of solar irradiance that are critical to concentrating solar technologies, especially Direct Normal Irradiance (DNI) and Plane of Array (POA), and current high-density ground telemetry is still expensive for many solar power plants.

Project Innovation

The purpose of this project is to develop and validate tools capable of monitoring and forecasting DNI and POA irradiance and the power generation accurately, from 5 minutes out to 72 hours in the future, at the Ivanpah Solar Thermal plant as well as at the California Valley Solar Ranch (CVSR) plant. The project also included the development of tools for predicting wind speed, which affects the heliostats' deployment, and the improvement of the power generation forecast via Resource-to-Power Model (RTP) for Ivanpah (CSP) and CVSR (Tracking PV) plants. The goal of this system is to reduce uncertainties associated with operation, regulation, and scheduling.

Project Benefits

This project introduced a new generation of forecasting methods that fill in a technology gap in the prediction of DNI and POA irradiance as well as solar power generation from PV tracking and CSP. This critical need is evident by the relative scarcity of DNI forecasting algorithms discussed in the scientific literature and the absence of DNI information from the majority of numerical weather prediction models. The development of a network of low-cost sensors for distributed monitoring at California Valley Solar Ranch (CVSR) provides a solution to the need for high-density ground telemetry at low cost. The devices provide an unprecedented level of irradiance sensor density. Forecasting research not only enhances the ability of power plant managers, utility companies and the California ISO to reduce solar costs to ratepayers, but it can also enable higher penetration of renewables.

Lower Costs


The project developed and validated models that lead to lower operation costs and consumer cost per solar kWh due to increased ability to absorb short-term ramps and maintain solar production, better utilization of ancillary gene

Economic Development

Economic Development

The forecasting tools developed in this project will help economic development by reducing solar power plant operating cost by more than 10% and having the ability to substantially affect the effective solar capacity in Californi

Environmental & Public Health

Environmental Sustainability

The project will result in GHG emission reductions by decreasing the uncertainty associated with solar power generation and diminishing the need of fossil fuel-based generation.

Greater Reliability


The tools developed in this project result in greater reliability by developing high-fidelity models that increase the accuracy of solar energy forecasting to decrease the number of forced outages and associated ancillary reserve

Key Project Members

Project Member

Carlos Coimbra


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Itron, Inc. dba IBS

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