Lorenzo talk on GPU computing for cloudiness tracking and prediction
Tony Lorenzo - IES Renewable Power Forecasting Group
Solar power forecasting, data assimilation, and El Gato.
Forecasting partners with regional power supply companies.
- Solar variability
Early AM full power as panels track sun
PM variability as clouds move across the sky.
- Irradiance to power consumption
- Clear-Sky Index
- Clear-sky expectation
Observation
TEP asked for solar forecasts becasue they saw variability as in issue.
Deployed irradiance sensor network
Network forecasts
Using GEOS
GHI - conversion of cloudiness to transmittance
Comparing irradiance forecasts errors
their errors are on the order of 150 (clearsky) to 120 (WA-WRF) to 50 (sensor network) W/m2
Satellite imae after optimal interpolation.
Satellite derived GHI estimate
Two conversio nmodels
a semi-empirical model that applies a regression to the data of visible images
a physical model that estimates cloud properties and performs radiative transefer
Nominal 1km resolution Adjusted visible albedo
Using 75km x 82 km area over tucson
Optimal interpoliation
Bayesian technique for minimizing the se distance between the field and observed
best linear unbiased estimated
Kalman filter
x_b = x_t + g
g~ N(0,P)
Obs
y=Hx_t +e
e ~ N(0,R)
x_a=x_b + W(y -H_x_b)
W= PH^T (R+HPH^T)^-1
Error covariances P and R
decompose P into a diagonal variance matrix and correlation matrix: P=D^0.5 C D^0.5
prescribe a correlation between the image pixels based on he difference incloudiness to construct C a
Compute D form cloud free training images
Assume observation arros from the sensors are uncorrelated
Used Python on elgato
import numpy as np
import scipy import linalg
import skcuda.linalg as cu
from pycuda import gpuarray
Dask
Numba
Singularity
PyCUDA
scikit-cuda
Sumatra - provenance tracking.
Travis Hardy - data prediction
Ensemble Kalman Filter