光伏发电预测的准确性对电力市场交易中新能源发电项目的收益至关重要。然而，由于光伏发电具有高度随机波动性和间歇性特点，难以构建高性能的光伏发电预测模型。本研究提出了一种多步光伏发电短期混合预测模型，该模型结合了改进的麻雀搜索算法、模糊c均值算法（FCM）、改进的自适应噪声完全集成经验模态分解（ICCEMDAN）、和条件时间序列生成对抗网络（CTGAN）。首先，提出了一种基于FCM和数据包络理论的数据聚类新方法，根据相似的功率模式划分数据集，并通过改进的麻雀搜索算法对参数进行优化。其次，结合灰色关联分析和特征创建，确定预测日的最佳相似日。此外，利用ICCEMDAN对原始光伏功率时间序列进行分解，并通过样本熵重构分量，以降低预测模型的计算成本。最后，使用 Wasserstein 距离、梯度惩罚和 Metropolis-Hastings 来保证 CTGAN 训练的稳定性。根据实验结果可以得出结论，数据包络聚类方法比气象因素更合理地划分数据集。麻雀搜索算法的优化增加了其全局和局部优化能力，进一步提升了FCM的性能。使用 CTGAN 生成近似真实数据分布的真实数据，可以训练出性能更好的预测模型，从而提高其对光伏功率波动的适应能力。所提出的混合模型基于不同季节、不同天气条件和来自不同地点的数据集进行了验证，结果证明了所提出的 38 步预测模型在准确性、应用和泛化能力方面优于本研究中涉及的其他模型.
The accuracy of photovoltaic power forecasting is crucial to the revenue of new energy generation projects in electricity market trading. However, due to the highly stochastic volatility and intermittent characteristics of photovoltaic power, it is difficult to construct high-performance photovoltaic power forecasting models. In this study, a multi-step short-term hybrid prediction model of photovoltaic power is proposed, which combines an improved sparrow search algorithm, Fuzzy c-means algorithm (FCM), improved complete ensemble empirical mode decomposition with adaptive noise (ICCEMDAN), and conditional time series generative adversarial networks (CTGAN). First, a new data clustering method based on FCM and data envelope theory is proposed to divide the dataset based on similar power patterns, and the parameters are optimized by an improved sparrow search algorithm. Second, the grey relational analysis and feature creation are combined to determine the optimal similar day for the forecasting day. Furthermore, the original photovoltaic power time series is decomposed using ICCEMDAN, and the components are reconstructed by sample entropy to reduce the computational cost of forecasting models. Finally, Wasserstein distance, gradient penalty, and Metropolis-Hastings are used to ensure CTGAN training stability. According to the experimental results, it can be concluded that the data envelope clustering method divides datasets more reasonably than meteorological factors. The optimization for the sparrow search algorithm increases its global and local optimization ability to further enhance the performance of FCM. Using CTGAN to generate realistic data that approximates real data distributions can train predictive models with better performance, which increases their adaptability to PV power fluctuations. The proposed hybrid model is validated based on different seasons, different weather conditions, and datasets from different locations, and the results demonstrate the advantage of the proposed 38-step predictive model in accuracy, application, and generalization capabilities over other models involved in this study.
