Photovoltaic Power Forecasting: Assessment of the Impact of Multiple Sources of Spatio-Temporal Data on Forecast Accuracy
Photovoltaic Power Forecasting: Assessment of the Impact of Multiple Sources of Spatio-Temporal Data on Forecast Accuracy
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DOI：
10.3390/en14051432
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发表时间：
2021-03-01
期刊：
影响因子：
3.2
通讯作者：
Kariniotakis, Georges
中科院分区：
文献类型：
--
作者：
Agoua, Xwegnon Ghislain;Girard, Robin;Kariniotakis, Georges
光伏发电在能源系统中的有效集成取决于预测其变化性的能力，即提供准确预测的能力。近年来，焦点从处理单个发电厂的经典预测方法转移到时空方法，其中地理上分散的数据被用作输入，以改进对未来6小时内地平线的预测。这些时空方法根据可用的数据源提供了不同的性能，但仍然没有评估每个数据源对实际预测性能的影响问题。在本文中，我们提出了一个灵活的时空模型来生成6h前的水平光伏生产预测，并利用该模型评估了不同的时空数据源对预测精度的影响。考虑的来源是来自邻近光伏发电厂、当地气象站、数值天气预报和卫星图像的测量数据。性能的评估是使用一个真实世界的测试案例进行的，该测试案例具有大量的136个光伏发电厂。使用平均绝对误差和均方根误差对每个数据源的预测误差进行了评估。结果表明，邻近的光伏发电厂有助于在前三个小时实现约10%的预测误差，随后卫星图像有助于提前6小时在整个地平线上获得额外的3%的预测误差。数值预报数据显示，直到6小时，地平线没有改善，但对于更大的地平线是必不可少的。
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.