In the Objective tab the AC power measurements are displayed for the selected day and PV system (when available).
The forecast procedure predicts this time series using a set of predictors extracted from the NWP-WRF model published at the Meteogalicia server using the meteoForecast package. The set of predictors are defined with different scenarios that can be selected in the Predictors tab.
Once the day to be predicted, the PV system, the training method and the scenario have been chosen, define the length of the train time series (number of days of recent past measurements - method "previous" - or selected from database - methods "ks" and "kt") in the Result tab and click on the Forecast button.
The experimental data used in this tool belongs to 5 PV plants. Data recording started on April 17th, 2008, although the database for this tool is restricted to the period comprised between January 8th, 2009 and December 29th, 2010. The PV plants have been equipped with a monitoring system that records the power generated by each inverter every 5 seconds. However, the forecast procedure works with hourly averages of these measurements because the WRF-NWP provides hourly outputs. The figure shows the hourly averages of the AC power measurements.
point: value at the location of interest.
IDW: interpolated value (Inverse Distance Weighting) at the location of interest.
TRI: Terrain Ruggedness Index.
TPI: Topographic Position Index.
rough: Largest inter-cell difference of a central pixel and its surrounding cells.
timeDesv: Standard deviation of consecutive predictons for the same hour.
The red line represents the hourly AC power measured at the plant (when available).
The black line represents the prediction of the median (quantile 0.5).
The shaded area represents the prediction interval comprised between the quantiles 0.1 and 0.9.
previous: This method selects days immediately before the day to be predicted.
kt: This method selects days with the lowest absolute difference between the clearness index of the day to be predicted and the clearness index of each day included in the database.
ks: This method selects days with the lowest Kolmogorov-Smirnov distance between the empirical distribution function of the irradiance forecast for the day to be predicted and the empirical distribution function of the irradiance forecast for each day included in the database.