Day-ahead forecasting of solar power output from photovoltaic plants in the American Southwest

Highlights

We propose a day-ahead forecast methodology for hourly PV power output.

We evaluate the methodology using four years of data from two 1 MWp PV plants.

The methodology achieves forecast skill of 13%–23% over a persistence model.

Annual RMSE relative to the plant capacity is 10.2%–14.0%.

Interannual forecast performance variability is also analyzed.

Abstract

A forecasting method for hourly-averaged, day-ahead power output (PO) from photovoltaic (PV) power plants based on least-squares optimization of Numerical Weather Prediction (NWP) is presented. Three variations of the forecasting method are evaluated against PO data from two non-tracking, 1 MWp PV plants in California for 2011–2014. The method performance, including the inter-annual performance variability and the spatial smoothing of pairing the two plants, is evaluated in terms of standard error metrics, as well as in terms of the occurrence of severe forecasting error events. Results validate the performance of the proposed methodology as compared with previous studies. We also show that the bias errors in the irradiance inputs only have a limited impact on the PO forecast performance, since the method corrects for systematic errors in the irradiance forecast. The relative root mean square error (RMSE) for PO is in the range of 10.3%–14.0% of the nameplate capacity, and the forecast skill ranges from 13% to 23% over a persistence model. Over three years, an over-prediction of the daily PO exceeding 40% only occurs twice at one of the two plants under study, while the spatially averaged PO of the paired plants never exceeds this threshold.

Keywords

  • Forecasting;
  • Solar power output;
  • Photovoltaics;
  • Numerical weather prediction;
  • Interannual performance variability

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