A Community Gridded Atmospheric Forecast System for Calibrated Solar Irradiance

Transcription

1 A Community Gridded Atmospheric Forecast System for Calibrated Solar Irradiance David John Gagne 1,2 Sue E. Haupt 1,3 Seth Linden 1 Gerry Wiener 1 1. NCAR RAL 2. University of Oklahoma 3. Penn State University 4. WSI International Conference on Energy Meteorology, 24 June 2015

2 Motivation Gridded forecasting system: synthesis of numerical, statistical, and human weather forecasts on a regular grid Problem: current gridded forecasting systems are inadequate, proprietary, or too specialized Need: A community gridded forecasting system in which many different interpolation, blending, and correction methods can be tested under a common framework Proposed Solution: Gridded Atmospheric Forecast System (GRAFS) Initial application: solar irradiance forecasting

3 Goal Produce a gridded forecast of hourly average global horizontal irradiance (GHI) Improve raw model output with smart interpolation Correct the grid using site observations and statistical techniques Use GHI forecasts for solar power prediction Utility-scale solar farms Distributed solar installations Easily provide forecasts for new installations

4 GRAFS-Solar Framework NWP Models NAM WRF-Solar Initial Grid Interpolated to 4 km CONUS Grid 1-Hour Averaging Observations Current: Sacramento Municipal Utility District (SMUD) MADIS Operational Statistical Correction System DICast Point Correction applied to neighborhoods Research Statistical Correction System (PyGRAFS) Random Forest Gradient Boosted Regression Output Products Maps of solar irradiance Single point forecasts % of clear sky irradiance Future: Other met. variables

5 Grid Mechanics System can utilize multiple models Currently uses North American Mesoscale (NAM) model hourly out to 36 hours, 3-hourly from hours out Downscale raw model data to evenly spaced 4km grid Uses downward short-wave radiation flux at surface field Grid covers CONUS Initially, do simple interpolation of 3-hourly values to hourly value

6 Time Interpolation of Cloud Information Instantaneous Model Output 3-Hourly Model Output 11am 2pm 11am 2pm 11am 2pm 11am 2pm Simple interpolation to get hourly averaged irradiance Smart interpolation based on time of day and cloud cover

7 Grid Correction Techniques (point forecast method) Difference between the tuned point forecast and the nearest grid forecast is calculated Neighbor look-up table based on distance and terrain is used to interpolate the differences to surrounding grid points Interpolated differences are added back onto the grid forecasts Point forecasts (MADIS sites) used to influence the grid in the Southwest US

8 Forecast Generation A new forecast is generated every hour Currently hourly out to 72 hours. Warmer colors = higher irradiance, cooler colors = lower irradiance Statistical corrections made only in defined neighborhood of each observing site Can create discontinuities in grid

9 GRAFS Site Verification RMSE NAM (blue) vs GRAFS (green) Forecasts NAM (blue) vs GRAFS (green)

10 Challenge: Observation Differences MADIS Solar radiation observations use a variety of averaging intervals and instruments

11 PyGRAFS Machine Learning Framework NWP model output on 4 km CONUS grid NWP model output at observation sites Spatial statistics about grid neighborhood Metadata about location, time, elevation Machine Learning Models Random Forest Ensemble of randomized decision trees Merged forecast data Train ML Model at all observation sites Merged training data Gradient Boosted Regression Weighted decision tree ensemble Predict GHI at every grid point PyGRAFS uses scientific Python libraries including pandas and scikit-learn Linear Regression Weighted sum of input variables

12 SMUD Verification Training machine learning models over similar observation sites in a smaller area results in large decrease in error.

13 Southwest US Verification NAM shows peaks in error in morning and afternoon. Random forest and gradient boosting regression are able to remove peaks and reduce error at those times Biases in NAM are due to offsets in diurnal solar radiation curve at many sites. Random forest and gradient boosting can correct those biases.

14 Forecast Comparison Some bias and terrain corrections Extensive terrain corrections More bias correction, less terrain

15 Summary GRAFS is a community gridded forecasting system led in development by NCAR RAL Operational system generating hourly solar radiation forecasts over US Research system uses spatial data processing and machine learning to produce improved forecasts over full grid

16 Next Steps Creating or finding a better GHI analysis Developing a scalable way to calculate solar position information Temporal features and additional input variables Adding more models and blending them Interested in collaborating? Contact Sue Haupt haupt@ucar.edu