NEXT : How it works

The solar resource is intermittent and subject to fluctuating weather conditions. Accurate forecasting is therefore essential to optimize the development of solar energy. A high-performance, multi-horizon forecasting API helps developers optimize their operations by predicting how much solar irradiance will be available at a specific time and location.

Solar forecasts rely on a combination of satellite data, weather models, and ground-based measurements. Satellite data provides detailed information on current cloud cover and solar irradiance, while weather models predict the large-scale movement of weather systems and their impact on future solar irradiance. Ground-based measurements provide insight into local weather conditions and the real-time output of the solar plant. These input data are continuously updated to ensure forecast accuracy and relevance.

To ensure optimal operation of solar plants, there are three types of solar forecasts, classified by their time horizon: near real-time, short-term, and long-term forecasts.

Very short-term forecasts, also known as Nowcasting, cover a time horizon of a few minutes and typically require a sky observation system using hemispherical cameras. They complement standard on-site sensor measurements and satellite imagery, which is less precise. This type of forecast is particularly useful for hybrid microgrid systems in isolated locations, where the alternative to PV is not instantly available, or when there are critical energy constraints (e.g., industrial sites).

Short-term forecasts (intra-day) predict solar irradiance for the upcoming hours of the day. They are crucial for enabling solar energy developers to optimize operations in real time and for grid operators to manage solar energy integration into the network.

Long-term forecasts (day-ahead) predict solar irradiance over several days to weeks. These are essential for solar energy stakeholders to plan operations and make decisions to balance energy supply and demand, ensuring a reliable power supply.

Weather models and satellite imagery are excellent sources of data for solar forecasting and are available worldwide. However, their temporal and spatial resolution is insufficient to detect cloud behavior at very short timescales and site-specific levels.

Monitoring sensors typically available on-site provide highly valuable insights into the real-time status of the system with much greater precision. CalibSun’s forecasting algorithm is able to integrate these measurements to continuously adjust forecasts based on actual data—especially when other data sources fail to anticipate short-term variability accurately.

CalibSun retrieves real-time data through secure pipelines. Whenever possible, real-time data collection is strongly recommended, as it significantly improves forecasting performance over short-term horizons.