In this document
We will introduce the importance of solar power forecasting in PV projects and outline the key concepts we at Solargis employ to provide forecasting services.
Introduction to solar power forecasting
As the world increasingly relies on renewable energy sources, solar power has become a crucial component of the global energy mix. However, integrating solar energy into the grid poses challenges due to its intermittent nature, which is heavily influenced by weather conditions. This is where solar power forecasting plays a vital role. Accurate forecasting is essential for maintaining grid stability, optimizing energy trading, and ensuring the economic viability of solar projects.
Importance of solar power forecasting
Grid Stability: Forecasting helps Distribution System Operators (DSOs) and Transmission System Operators (TSOs) balance energy supply and demand, preventing power imbalances and potential blackouts
Economic Benefits: Accurate forecasts reduce financial penalties for PV asset operators by ensuring that the generated power aligns with contractual obligations. It also enables traders to make informed decisions, maximizing profits in energy markets
Operational Efficiency: Forecasting aids in sizing battery energy storage systems effectively, smoothing power output fluctuations, and scheduling maintenance during periods of low energy production
Best practices in solar power forecasting
Following is a list of best practices for optimal solar power forecasting service:
Combining forecast models: Utilize a combination of different models (e.g., Cloud motion vector and Numerical weather prediction model) to leverage their strengths.
Temporal resolution and forecast horizon: Provide forecasts with appropriate temporal resolutions (e.g., hourly or 15-minute) and forecast horizons (from intraday up to 14 days) to meet specific operational needs.
Reference data quality control: When evaluating forecast accuracy against reference data (actual measured solar radiation or PV power output), ensure the reference data is quality-controlled to provide a real picture of the forecast accuracy.
Forecast accuracy evaluation: Use consistent metrics such as Mean bias deviation (MBD), Mean absolute deviation (MAD), or Root mean square deviation (RMSD) to assess forecast accuracy. Normalize these metrics to facilitate comparison across different PV power plants.
The Solargis approach
At Solargis, we employ a comprehensive approach to solar power forecasting, combining advanced models and rigorous data quality control to provide accurate and reliable forecasts. Our process is designed to support efficient grid management, optimize energy trading, and enhance the economic viability of solar projects.
By following this structured approach, we provide high-quality forecasting services that enhance the efficiency and profitability of solar energy projects.
Forecast model selection and combination
Cloud Motion Vector (CMV) Model: Utilized for short-term predictions (up to 3 hours), this model tracks cloud movements using satellite imagery to predict immediate changes in solar radiation.
Numerical Weather Prediction (NWP) Models: These models simulate atmospheric conditions over longer horizons, providing accurate forecasts beyond 3 hours and up to 14 days ahead.
Combining CMV and NWP models allows us to leverage their strengths, offering more accurate and reliable forecasts across different time horizons.
Forecast generation and delivery
Forecasts are generated in a Time Series format, which facilitates easy integration into operational systems. Temporal resolutions can vary from 5-minute to hourly or daily intervals, depending on customer needs.
Forecast horizons extend from Nowcast (minutes to a few hours ahead) to 14 days, enabling both short-term operational decisions and medium-term strategic planning.
Data collection and quality control
To evaluate the accuracy of our forecast, we collect historical and real-time data from various sources, including satellite imagery and ground measurements.
Ensuring data quality is crucial; We rigorously check the data we use to evaluate our models and to ensure a high-quality baseline and accurate performance assessment.
Forecast accuracy evaluation
We use metrics such as Mean Bias Deviation (MBD), Mean Absolute Deviation (MAD), and Root Mean Square Deviation (RMSD) to assess forecast accuracy.
Normalizing these metrics against installed capacity or AC power limitation enables comparison across different PV power plants.
We regularly perform these evaluations to improve our services, using high-quality ground-measured data as a reference for accuracy.
Comparing Solargis with leading solar power forecasting providers
The solar power forecasting market is competitive, with several reputable providers offering advanced services. Here, we compare Solargis with some of the leading competitors, highlighting key features and benefits.
Key Competitors
Solargis: Utilizes a combination of cloud motion vectors (CMV) and numerical weather prediction (NWP) models, offering flexible temporal resolution (from 5 minutes to daily) and an extended forecast horizon of up to 14 days. Data is delivered via API, SFTP, email, and GIS formats, with global coverage.
Reuniwatt: Specializes in satellite and NWP-based solar radiation forecasting, providing temporal resolution from 15 minutes to daily and a forecast horizon up to 7 days. Data is delivered through API, and the service is available globally.
Meteologica: Integrates sky cameras, satellite imagery, and NWP models to deliver high-frequency (as frequent as every minute) to hourly forecasts. The forecast horizon is up to 8 days for site-specific forecasts and 40 minutes for ultra-short-term. Data is accessible via API, email, and GIS, with global reach.
Solcast: Employs satellite and NWP models, offering 5-minute to daily temporal resolution and a forecast horizon up to 14 days. Data is delivered primarily through API, with worldwide coverage.
Enercast: Uses NWP models, providing 15-minute resolution and the longest forecast horizon among the group—up to 31 days. Data is delivered via API, SFTP, and email, with global availability.
Feature | Solargis | Reuniwatt | Meteologica | Solcast | Enercast |
|---|---|---|---|---|---|
Forecast models | CMV + NWP | Satellite + NWP | Sky camera + Satellite + NWP | Satellite + NWP | NWP |
Temporal resolution | 5-min to daily | 15-min to daily | 1-min to hourly | 5-min to daily | 15-min |
Forecast horizon | Up to 14 days | Up to 7 days | Up to 8 days (site-specific), 40 min (ultra-short-term) | Up to 14 days | Up to 31 days |
Data delivery method | API, SFTP, email, GIS | API | API, email, GIS | API | API, SFTP, email |
Global coverage | Yes | Yes | Yes | Yes | Yes |
Key differentiators
Each provider offers unique strengths in solar power forecasting, but several key differences emerge:
Solargis stands out for its comprehensive model combination (cloud motion vectors plus NWP), which enhances both short-term and medium-term forecast accuracy. Its flexible temporal resolution (5-min to daily) and extended forecast horizon (up to 14 days) cover both operational and trading needs. The variety of data delivery methods (API, SFTP, email, GIS) ensures seamless integration into diverse workflows, and global coverage is standard.
Reuniwatt excels in detailed solar radiation forecasting using satellite imagery and NWP, making it suitable for high-resolution, site-specific applications. However, its temporal resolution starts at 15 minutes, and the forecast horizon is limited to 7 days.
Meteologica offers ultra-short-term forecasts (down to every minute) using sky cameras and provides medium-term forecasts through satellite and NWP models. This makes it ideal for operators requiring real-time responsiveness. Its site-specific forecast horizon is up to 8 days, but ultra-short-term forecasts are limited to 40 minutes.
Solcast provides high-frequency forecasts (5-min to daily) and matches Solargis in forecast horizon (up to 14 days), but relies solely on satellite and NWP models, without the added benefit of cloud motion vector integration.
Enercast offers the longest forecast horizon (up to 31 days) and 15-minute resolution, focusing on longer-term planning. However, it uses only NWP models, which may not capture rapid cloud dynamics as effectively as hybrid approaches.
Solargis uniquely combines the strengths of both cloud motion vectors and NWP models, resulting in superior short- and medium-term forecast accuracy. Its flexible temporal resolution, extended forecast horizon, and multiple delivery formats make it adaptable for a wide range of applications—from real-time plant operations to market trading. While other providers excel in specific areas (e.g., ultra-short-term, site-specific, or long-term forecasts), Solargis offers the most balanced and versatile solution, supporting both operational reliability and commercial decision-making. This comprehensive approach positions Solargis as the leading choice for organizations seeking accuracy, flexibility, and seamless integration in solar power forecasting.