Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5450521 | Solar Energy | 2017 | 7 Pages |
Abstract
In this study, we analyze various day-ahead production forecasts for a 1.86 MW photovoltaic plant considering different techniques and sets of inputs. A nRMSE of 22.54% was obtained for a Support Vector Regression model trained by numerical weather predictions (NWP). This model produced the most benefits. An annual forecasting value of 4788⬠with respect to a persistence model was obtained for trading in the Iberian (Spain and Portugal) day-ahead electricity market. Annual value added by the NWP service totaled 2801⬠and room for improvement regarding NWP variables rose to 3877â¬. As a general trend, it was found that smaller errors (RMSE) generated higher incomes. For each 1 kW h improvement in RMSE, the annual value of forecasting increased 22.32â¬. Nevertheless, some models that gave larger errors than others also brought greater benefits. Thus, market conditions must be considered to accurately evaluate model economic performance.
Keywords
Related Topics
Physical Sciences and Engineering
Energy
Renewable Energy, Sustainability and the Environment
Authors
J. Antonanzas, D. Pozo-Vázquez, L.A. Fernandez-Jimenez, F.J. Martinez-de-Pison,