Key Takeaways
- Bidding incorrectly in the DAM leads to an imbalance penalty.
- NWP + historical data + temperature coefficient is the best forecasting trio.
- Risk is managed asymmetrically with P10-P50-P90 confidence intervals.
- The hybrid ML model minimizes sudden cloud-cover errors.
One of the biggest commercial risks for solar power plants is imbalance costs in the energy market. A deviation of the day-ahead bid from actual production can cause serious financial burdens. High-accuracy production forecasting is the only way to minimize this risk.
The Day-Ahead Market and the Imbalance Mechanism
In the Turkish electricity market, the Day-Ahead Market (DAM) is the platform where producers declare the next day's hourly production. If actual production is more or less than the bid quantity, an imbalance charge is applied in the system's direction. Even a 10% deviation can create a cost that reaches hundreds of thousands of liras.
The Core Inputs of the Forecasting Model
A high-accuracy PV production forecast model is based on this data:
- Numerical Weather Prediction (NWP): GFS, ECMWF or local models; forecasts wind, cloud cover and irradiance intensity hourly.
- Plant Historical Data: The last 12-24 months of production data lets the model learn your facility's real behavior.
- Panel Temperature Coefficient: Panel efficiency drops at high temperatures; if this effect is not included in the model, serious forecast errors occur in summer.
- Soiling and Shading Model: Seasonal panel soiling, dust and local obstacles affect production.
The Architecture of the SolarTools Forecast Engine
SolarTools uses hybrid machine-learning models trained specifically for each plant. On top of a physics-based base model (POA irradiance → DC production → AC output), a correction layer that learns from historical deviation data is added. This structure minimizes the margin of error especially during sudden cloud transitions.
Risk Management with Confidence Intervals
A single point forecast is not enough. The SolarTools forecast engine produces P10-P50-P90 confidence intervals. Strategically positioning the bid quantity within this interval provides the ability to manage imbalance risk asymmetrically.
Real-World Success
At plants using an accurate forecast engine, significant reductions in DAM imbalance charges are observed. This saving can reach millions of liras per year, especially at large-capacity plants.
Frequently Asked Questions
How far in advance does the forecast engine predict production?
SolarTools produces a D-1 (one day ahead) hourly forecast for the DAM. Optionally, forecasts extending up to 72 hours can also be provided.
Where does the weather forecast data come from?
It is used by blending global numerical weather models such as GFS and ECMWF with real-time data from local weather stations.
How is forecast accuracy measured?
It is measured with the nRMSE (normalized root mean square error) and nMAE metrics. The target: keeping the hourly nRMSE below 8%.
Explore More
Browse our other solutions and resources to accelerate your energy journey.
Ücretsiz Dene