Zone 3 CRPS

This report of solar forecast accuracy was automatically generated using the Solar Forecast Arbiter. Please see our GitHub repository for known issues with the reports or to create a new issue.

Report Metadata

  • Name: Zone 3 CRPS
  • Start: 2020-01-01 08:00:00+00:00
  • End: 2020-06-30 23:59:00+00:00
  • Generated at: 2020-07-07 01:19:04+00:00

Data

This report includes forecast and observation data available from 2020-01-01 08:00:00+00:00 to 2020-06-30 23:59:00+00:00.

Observations and Forecasts

The table below shows the observation, forecast, and reference forecast triplets analyzed in this report. The reference forecast is listed as None if it was not selected during the report configuration. The table includes the unprocessed observation, forecast, and reference forecast interval label (beginning, ending, instantaneous) and interval length. If these quantities do not match, the Solar Forecast Arbiter must align and/or resample the data before computing error statistics. The Solar Forecast Arbiter aligns the observation data to the forecast data. Reference forecasts must have the same interval label, length, and type as the associated forecasts. The aligned and resampled parameters are also shown below, including a unique name for the aligned and resampled observation/forecast pair that appears in the metrics plots, tables, and CSV download.

Table of data alignment parameters
Aligned Pairs Observations Forecasts Reference Forecasts
Name Interval Label
Interval Length
Name Interval Label
Interval Length
Name Interval Label
Interval Length
Name Interval Label
Interval Length
Desert Rock NV Day Ahead GEFS ghi ending
60 min
Desert Rock NV ghi ending
1 min
Desert Rock NV Day Ahead GEFS ghi ending
60 min
None
Albuquerque New Mexico Day Ahead GEFS ghi ending
60 min
Albuquerque New Mexico ghi ending
1 min
Albuquerque New Mexico Day Ahead GEFS ghi ending
60 min
None
Hanford California Day Ahead GEFS ghi ending
60 min
Hanford California ghi ending
1 min
Hanford California Day Ahead GEFS ghi ending
60 min
None
University of Arizona OASIS Day Ahead GEFS ghi ending
60 min
University of Arizona OASIS ghi ending
1 min
University of Arizona OASIS Day Ahead GEFS ghi ending
60 min
None
University of Nevada Las Vegas Day Ahead GEFS ghi ending
60 min
University of Nevada Las Vegas ghi ending
1 min
University of Nevada Las Vegas Day Ahead GEFS ghi ending
60 min
None

The plots below show the realigned and resampled time series of observation and forecast data as well as the distribution of forecast vs observation data.

Controls to pan, zoom, and save the plot are shown on the right. Clicking on an item in the legend will hide/show it.

Data Validation

The Solar Forecast Arbiter applied its data validation toolkit to the observation data. The table below shows the selected filters and the number of observations that were removed from each forecast/observation pair.

Table of data validation results
Aligned Pair Desert Rock NV Day Ahead GEFS ghi Albuquerque New Mexico Day Ahead GEFS ghi Hanford California Day Ahead GEFS ghi University of Arizona OASIS Day Ahead GEFS ghi University of Nevada Las Vegas Day Ahead GEFS ghi
Observation Desert Rock NV ghi Albuquerque New Mexico ghi Hanford California ghi University of Arizona OASIS ghi University of Nevada Las Vegas ghi
NIGHTTIME 133058 133092 133075 133240 133071

These intervals were removed from the raw time series before resampling and realignment. For more details on the data validation results for each observation, please see the observation page linked to in the table above.

Data providers may elect to reupload data to fix issues identified by the validation toolkit. The metrics computed in this report will remain unchanged, however, a user may generate a new report after the data provider submits new data. The online version of this report verifies that the data was not modified after the metrics were computed.

Data Resampling and Alignment

The Solar Forecast Arbiter's preprocessing applies the following operations to the data:

  1. Apply the data validation tests and exclude the matched data.
  2. For deterministic forecasts with interval lengths that are longer than the observations interval lengths,
    1. Resample the observations to the forecast using the mean.
    2. Remove resampled observation data if more than 10% of the points in the resampled interval are missing. For example, if 1-minute observations are resampled to 60-minute means, then a 60 minute period must have no more than 6 minutes of missing data.
  3. Remove times that do not exist in both the resampled observations, the forecasts, and, if selected, the reference forecasts.
The table below summarizes the data preprocessing results.

Table of data preprocessing results
Preprocessing Description Desert Rock NV Day Ahead GEFS ghi
Number of Points
Albuquerque New Mexico Day Ahead GEFS ghi
Number of Points
Hanford California Day Ahead GEFS ghi
Number of Points
University of Arizona OASIS Day Ahead GEFS ghi
Number of Points
University of Nevada Las Vegas Day Ahead GEFS ghi
Number of Points
TOTAL FLAGGED VALUES DISCARDED 133429 133128 135454 133240 133072
Total Forecast Values Dropped 0 0 0 0 0
ProbabilisticForecast Values Discarded by Alignment 1832 1901 1889 1868 1834
Observation Values Discarded by Alignment 220 149 201 159 206
ProbabilisticForecast Undefined Values 0 0 0 0 0
Observation Undefined Values 0 0 0 0 0

Users may wish to fix the data issues by having new or missing data uploaded. The metrics computed in this report will remain unchanged, however, a user may generate a new report after the data provider submits new data.

Metrics

The table below shows the normalization, deadband, and cost parameters used in calculating the metrics. The normalization factor is applied when calculating the metrics NMAE, NMBE, and NRMSE. The normalization is in the same units as the forecast and observation. By default, AC power forecasts are normalized by AC capacity and DC power forecasts are normalized by DC capacity. Normalization for all other forecasts is undefined, and the metric values are set to nan. The deadband accounts for observation uncertainty by setting the error (forecast - observation) equal to 0 for any point that is within the deadband. The error is unchanged for any point that is outside the deadband. The deadband is specified as a percentage of the observation value at each time. A value of None indicates that no deadband was applied for that observation/forecast pair. The deadband is accounted for in the following metrics: MAE, MBE, RMSE, MAPE, NMAE, NMBE, NRMSE, Skill, Cost. It is ignored for all other metrics. The Cost metric is calculated using the set of cost models defined by cost parameters. See the solarforecastarbiter-core documentation for a description of how these cost parameters are used to calculate Cost.

Table of metrics metadata
Name Normalization Deadband (%) Cost Parameters
Desert Rock NV Day Ahead GEFS ghi nan None
Albuquerque New Mexico Day Ahead GEFS ghi nan None
Hanford California Day Ahead GEFS ghi nan None
University of Arizona OASIS Day Ahead GEFS ghi nan None
University of Nevada Las Vegas Day Ahead GEFS ghi nan None

A table of metrics over the entire study period and figures for the selected categories are shown below. Metrics may be downloaded in CSV format by clicking here.

Table of total metrics
Forecast CRPS
Desert Rock NV Day Ahead GEFS ghi 21.7
Albuquerque New Mexico Day Ahead GEFS ghi 29.6
Hanford California Day Ahead GEFS ghi 19.3
University of Arizona OASIS Day Ahead GEFS ghi 17.5
University of Nevada Las Vegas Day Ahead GEFS ghi 18.3

Total Analysis

Metric totals for the entire selected period.


Year Analysis

Metrics per year.


Month Of The Year Analysis

Metrics per month.


Hour Of The Day Analysis

Metrics per hour of the day.


Date Analysis

Metrics per individual date.


Versions

This report was created using open source software packages. The relevant packages and their versions are listed below. Readers are encouraged to study the source code to understand exactly how the data was processed.

Package Version
solarforecastarbiter 1.0rc1
pvlib 0.7.2
pandas 1.0.3
numpy 1.18.2
scipy 1.4.1
statsmodels 0.11.0
plotly 4.5.3
bokeh 1.4.0
netcdf4 1.5.3
xarray 0.15.0
tables 3.6.1
numexpr 2.7.1
bottleneck None
jinja2 2.11.2
python 3.7.8
platform Linux-3.10.0-957.5.1.el7.x86_64-x86_64-with-debian-10.4