Forecasting Renewables

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


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.

Let’s talk about forecasting renewables