Forecasting Renewables | Logic20/20

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


Solar panels

Accurate renewable energy forecasting is critical

The White House

The U.S. government has set a goal of reaching

100% carbon pollution–free electricity

by 2035.1

Man installing solar panels

Solar made up

45% of new electricity generation capacity

added in the United States through the first three quarters of 2022.2

Wind turbines on hills

The U.S. wind industry installed over


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

Dark clouds

weather patterns

Weather satellite

Satellite images

Weather station

Weather station

Smart sensor on solar panel

Smart sensors
on renewables

Man looking through binoculars

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.



Learn more about grid intelligence
Row of wind turbines



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