标题： 基于多种异构数据源的短期光伏发电预测 显示英文标题

标题： Short-term photovoltaic generation forecasting using multiple heterogenous sources of data

摘要： 可再生能源（RES）的渗透率正在迅速提高：在法国，光伏（PV）发电的装机容量从2007年的26MW增加到2017年的8GW [1]。 由于光伏电站产生的电力高度依赖于变化的天气条件，这种持续增长的速度正在引发关于电网稳定性和收益优化的问题。 为克服这些障碍，光伏预测成为了一个研究热点。 在本文中，我们提出了一种低复杂度的预测模型，能够使用多种异构数据源（电力测量、卫星图像和数值天气预报（NWP））进行操作。 作为一种非参数模型，该模型可以扩展以包含输入。 所提出模型的主要优势在于其能够根据所需的预测时间范围（从15分钟到6小时 ahead）自动选择最佳的数据源，这是通过特征选择过程实现的。 为了利用日益增多的光伏电站，实施了一种时空（ST）方法。 这种方法考虑了空间分布的电站之间的依赖关系。 每个数据源都进行了逐步研究，以便量化它们对预测性能的影响。 多种数据源的使用将预测性能提高了多达40%（以RMSE衡量）相比参考模型。 评估过程是在九个来自罗纳国家公司（CNR）的光伏电站上进行的。 显示英文摘要

摘要： Renewable Energies (RES) penetration is progressing rapidly: in France, the installed capacity of photovoltaic (PV) power rose from 26MW in 2007 to 8GW in 2017 [1]. Power generated by PV plants being highly dependent on variable weather conditions, this ever-growing pace is raising issues regarding grid stability and revenue optimization. To overcome these obstacles, PV forecasting became an area of intense research. In this paper, we propose a low complexity forecasting model able to operate with multiple heterogenous sources of data (power measurements, satellite images and Numerical Weather Predictions (NWP)). Being non-parametric, this model can be extended to include inputs. The main strength of the proposed model lies in its ability to automatically select the optimal sources of data according to the desired forecast horizon (from 15min to 6h ahead) thanks to a feature selection procedure. To take advantage of the growing number of PV plants, a Spatio-Temporal (ST) approach is implemented. This approach considers the dependencies between spatially distributed plants. Each source has been studied incrementally so as to quantify their impact on forecast performances. This plurality of sources enhances the forecasting performances up to 40% in terms of RMSE compared to a reference model. The evaluation process is carried out on nine PV plants from the Compagnie Nationale du Rh{\^o}ne (CNR).