Forecasting Renewables
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New solutions to renewable forecasting challenges
Because they depend on weather, renewable sources such as solar and wind are inherently challenging to predict. In contrast to traditional statistically based methods for forecasting renewables, AI and machine learning solutions can tap into hundreds of data sources in near real time and are capable of training—and re-training—themselves, resulting in
• More accurate forecasts based on up-to-the-minute data
• Reduced need for specialized expertise
• Faster adaptation to changing environmental factors
Accurate renewable energy forecasting is critical
Solar made up
45% of new electricity generation capacity
added in the United States through the first three quarters of 2022.2
The U.S. wind industry installed over
13,000
megawatts
of new wind capacity in 2021—the second-highest amount installed in one year.3
Our approach
AI and machine learning platforms can mine data in real time from a wide array of sources, including
Historical
weather patterns
Satellite images
Weather station
measurements
Smart sensors
on renewables
Observational inputs
Applications like Splight use advanced models to evaluate data from these and other sources in producing highly accurate forecasts of both supply and demand, enabling utilities to accommodate fluctuations and ensure an optimized flow of clean energy.
Over time, these platforms can identify patterns and use the resulting insights to update their models, eliminating the need for manual retraining.
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