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 (up to 14 days) to meet specific operational needs.
Reference data quality control: Ensure reference data (actual measured solar radiation or PV power output) is quality-controlled to accurately evaluate forecast accuracy.
Forecast accuracy evaluation: Use 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.
Operational forecast data delivery: Deliver forecast data in a time-series format to facilitate easy integration into operational systems. This format provides a sequence of data points issued at regular intervals.
Scheduling maintenance: Use meteorological forecasts to schedule maintenance during periods of low energy production, such as rainy or cloudy days, to minimize economic losses.
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.
Data collection and quality control
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 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 eeds.
Forecast horizons extend from Nowcast (minutes to a few hours ahead) to 14 days, enabling both short-term operational decisions and long-term strategic planning.
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.
Operational support and maintenance planning
Our forecasts support grid operators in maintaining supply-demand balance and help traders make informed decisions.
Meteorological forecasts are used to schedule maintenance during periods of low energy production, minimizing economic losses.
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
WeatherTech: Offers advanced weather forecasting solutions, focusing on precision and reliability for energy trading and grid management.
Reuniwatt: Specializes in solar radiation forecasting using satellite imagery and NWP models, providing detailed insights for solar energy optimization.
Vaisala: Provides comprehensive weather forecasting services, including solar irradiance forecasts, leveraging advanced NWP models and ground-based measurements.
Solcast: Specializes in high-resolution solar irradiance and PV power forecasting using advanced 3D satellite cloud modeling and machine learning, delivering global coverage with rapid updates and robust API integration for energy optimization.
Feature | Solargis | WeatherTech | Reuniwatt | Vaisala | Solcast |
|---|---|---|---|---|---|
Forecast Models | CMV + NWP | NWP | Satellite + NWP | NWP | 3D Satellite + ML + NWP |
Temporal Resolution | 5-min to daily | Hourly to daily | 15-min to daily | Hourly to daily | 5-min to daily |
Forecast Horizon | Up to 14 days | Up to 10 days | Up to 7 days | Up to 10 days | Up to 7 days |
Data Delivery Format | Time-series | Time-series | CSV, API | Time-series, API | API, Time-series, CSV |
Global Coverage | Yes | Yes | Yes | Yes | Yes |
Key Differentiators
Solargis stands out with its comprehensive model combination and extended forecast horizon, offering flexibility in temporal resolution and operational support.
WeatherTech focuses on precision for energy trading, with a strong emphasis on NWP models.
Reuniwatt excels in detailed solar radiation forecasting using satellite imagery.
Vaisala provides robust ground-based measurements alongside NWP forecasts.
Solcast differentiates itself through rapid-update, high-resolution forecasts using advanced 3D satellite cloud modeling and machine learning, enabling highly localized and responsive solar energy predictions.
Each provider has unique strengths, but Solargis's balanced approach and extended forecast capabilities make it a versatile choice for diverse solar energy applications.