Abstract
The efficient integration of photovoltaic (PV) production in energy systems is conditioned by the capacity to anticipate its variability, that is, the capacity to provide accurate forecasts. From the classical forecasting methods in the state of the art dealing with a single power plant, the focus has moved in recent years to spatio-temporal approaches, where geographically dispersed data are used as input to improve forecasts of a site for the horizons up to 6 h ahead. These spatio-temporal approaches provide different performances according to the data sources available but the question of the impact of each source on the actual forecasting performance is still not evaluated. In this paper, we propose a flexible spatio-temporal model to generate PV production forecasts for horizons up to 6 h ahead and we use this model to evaluate the effect of different spatial and temporal data sources on the accuracy of the forecasts. The sources considered are measurements from neighboring PV plants, local meteorological stations, Numerical Weather Predictions, and satellite images. The evaluation of the performance is carried out using a real-world test case featuring a high number of 136 PV plants. The forecasting error has been evaluated for each data source using the Mean Absolute Error and Root Mean Square Error. The results show that neighboring PV plants help to achieve around 10% reduction in forecasting error for the first three hours, followed by satellite images which help to gain an additional 3% all over the horizons up to 6 h ahead. The NWP data show no improvement for horizons up to 6 h but is essential for greater horizons.
Highlights
The urge of response to climate change and the necessity to reduce the global carbon footprint have put renewable energy in the spotlight
The model follows a data-driven approach, where the available data are directly fed as input without other advanced pre-treatment than normalization; (2) we show that the large dimensionality of the model can be efficiently addressed by a Lasso approach that permits to select the most relevant input; (3) we provide a thorough quantitative comparison of the impact that the multiple heterogeneous sources of spatio-temporal data have on the forecasting performance
For 15 min horizon, all the models show similar performances; for longer horizons the ST model outperforms the AR model; the integration of the local meteorological information reduce the MAE compared to when this info is not used; the model resulting from the combination of spatio-temporal and satellite data is the best model; the use of satellite data in combination with ST measured data results to more efficient forecasts for the short-term forecasting than the combination of ST and Numerical Weather Predictions (NWP); the level of the observed errors is similar to the lowest observed in the literature
Summary
The urge of response to climate change and the necessity to reduce the global carbon footprint have put renewable energy in the spotlight. The model follows a data-driven approach, where the available data are directly fed as input without other advanced pre-treatment than normalization ( i.e., to produce information like cloud motion vectors); (2) we show that the large dimensionality of the model can be efficiently addressed by a Lasso approach that permits to select the most relevant input; (3) we provide a thorough quantitative comparison of the impact that the multiple heterogeneous sources of spatio-temporal data have on the forecasting performance This data include measurements from neighboring PV plants, local meteorological stations, NWP forecasts and satellite images.
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