With the fast expansion of renewable energy systems during recent years, the stability and quality of smart grids using solar energy have been challenged because of the intermittency and fluctuations. Hence, forecasting photo-voltaic (PV) power generation is essential in facilitating planning and managing electricity generation and distribution. In this paper, the ultra-short-term forecasting method for solar PV power generation is investigated. Subsequently, we proposed a radial basis function (RBF)-based neural network. Additionally, to improve the network generalization ability and reduce the training time, the numbers of hidden layer neurons are limited. The input of neural network is selected as the one with higher Spearman correlation among the predicted power features. The data are normalized and the expansion parameter of RBF neurons are adjusted continuously in order to reduce the calculation errors and improve the forecasting accuracy. Numerous simulations are carried out to evaluate the performance of the proposed forecasting method. The mean absolute percentage error (MAPE) of the testing set is within 10%, which show that the power values of the following 15 min. can be predicted accurately. The simulation results verify that our method shows better performance than other existing works.

中文翻译：

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通过RBF神经网络进行光伏功率的超短期预测

近年来，随着可再生能源系统的快速发展，由于间歇性和波动性，使用太阳能的智能电网的稳定性和质量受到了挑战。因此，预测光伏（PV）发电量对于促进规划和管理发电及配电至关重要。本文研究了太阳能光伏发电的超短期预测方法。随后，我们提出了一种基于径向基函数（RBF）的神经网络。另外，为了提高网络泛化能力并减少训练时间，限制了隐层神经元的数量。在预测的功率特征中，将神经网络的输入选择为具有较高Spearman相关性的输入。对数据进行归一化，并连续调整RBF神经元的扩展参数，以减少计算误差，提高预测精度。进行了许多模拟，以评估所提出的预测方法的性能。测试集的平均绝对百分比误差（MAPE）在10％以内，表明接下来15分钟的功效值。可以准确预测。仿真结果验证了我们的方法具有比其他现有工作更好的性能。其中显示了以下15分钟的功率值。可以准确预测。仿真结果验证了我们的方法具有比其他现有工作更好的性能。其中显示了以下15分钟的功率值。可以准确预测。仿真结果验证了我们的方法具有比其他现有工作更好的性能。