A Multi-scale, Multi-Model, Machine-Learning Solar Forecasting Technology
- IBM, Yorktown Heights, NY (United States). Thomas J. Watson Research Center; IBM Research
The goal of the project was the development and demonstration of a significantly improved solar forecasting technology (short: Watt-sun), which leverages new big data processing technologies and machine-learnt blending between different models and forecast systems. The technology aimed demonstrating major advances in accuracy as measured by existing and new metrics which themselves were developed as part of this project. Finally, the team worked with Independent System Operators (ISOs) and utilities to integrate the forecasts into their operations.
- Research Organization:
- IBM, Yorktown Heights, NY (United States). Thomas J. Watson Research Center
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE)
- DOE Contract Number:
- EE0006017
- OSTI ID:
- 1395344
- Report Number(s):
- DE-EE--0006017
- Country of Publication:
- United States
- Language:
- English
Similar Records
Building the Sun4Cast System: Improvements in Solar Power Forecasting
RAVIS: Resource Forecast and Ramp Visualization for Situational Awareness of Variable Renewable Generation
Journal Article
·
Fri Jun 16 00:00:00 EDT 2017
· Bulletin of the American Meteorological Society
·
OSTI ID:1362154
RAVIS: Resource Forecast and Ramp Visualization for Situational Awareness of Variable Renewable Generation
Conference
·
Tue Jun 22 00:00:00 EDT 2021
·
OSTI ID:1810727