When estimating the spare capacity requirement for the operation of photovoltaic-hydrogen systems, the estimation error is relatively large due to the uncertainty of operation data of photovoltaic-hydrogen production system. Therefore, a prediction model based on nearest neighbor clustering is proposed for estimating the spare capacity requirement for the operation of photovoltaic-hydrogen systems. In this model, the uncertain natural nearest neighbor mechanism in the nearest neighbor clustering is introduced to classify the data points based on their density, sparsity, and noise. The data set is divided into different groups of uncertain photovoltaic-hydrogen production system operation data objects. After obtaining the uncertain natural stable state output results using the uncertain natural neighbor search algorithm, the noise points are removed based on the difference of eigenvalues. Then, the photovoltaic-hydrogen production system operation data is clustered using the uncertain natural neighbor density factor. In the construction phase of the estimation model, the radial symmetric Gaussian radial basis function (RBF) is used as the kernel function, and all RBF output results are mapped to the same space to obtain the photovoltaic-hydrogen production system operational reserve capacity requirement results. The testing results show that the proposed method has a maximum estimation error of less than 250 MW for the maximum reserve capacity requirement and a minimum estimation error of less than 150 MW for the minimum reserve capacity requirement, effectively reducing the energy management cost.