Abstract:

The large-scale grid integration of new energy sources such as wind power is an important measure to accomplish the goal of the ″double carbon″. Reliable wind power prediction is important to ensure the safe operation of the power grid. Therefore, an ultra-short-term wind power combination prediction model is proposed. First, the original sequence of wind power is screened for outliers and corrected. The corrected data validates the objective law. Second, the two-layer decomposition algorithm is used to decompose the original sequence. The application of the modal decomposition algorithm can achieve sub-sequences with more predictable trends, which reduces the difficulty of wind power prediction. Subsequently, the Multi-Objective Improved Slime Mould Algorithm-Support Vector Machines (MOISMA-SVM) is constructed to accurately predict the subsequences and perform additive reconstruction. MOISMA optimizes multiple objective functions while optimizing SVM parameters to obtain wind power prediction results. Finally, the MOISMA-SVM model is applied to further correct the absolute error of these predictions, with the error correction results added to the initial wind power forecasts to produce the final point predictions. Experimental results demonstrate that the proposed model achieves the best error metric performance across both datasets, with Mean Absolute Errors (MAE) of 0.505 7 MW and 0.672 6 MW, representing improvements of 98.79% and 98.50% over the baseline SVM model, respectively. This highlights the high accuracy and robustness of the proposed approach. Based on the point prediction results, an improved kernel density estimation interval prediction model is also established, which generates prediction intervals with high reliability and narrow bandwidth. The Coverage Width-based Criterion (CWC) values for the two datasets are 0.002 4 and 0.002 8, respectively, enabling a more precise characterization of wind power fluctuations and enhancing the overall practicality of the model.

Key words: wind power prediction, decomposition model, error correction, interval prediction, multi-objective optimization

摘要：

风电等新能源的大规模并网是完成"双碳"目标的重要措施之一, 而可靠的风电功率预测是保障电网安全运行的不可或缺的技术支撑。为此, 提出一种超短期风电功率点-区间预测模型。首先, 对风电功率原始序列进行异常值筛选以及修正, 让修正后的数据更符合客观规律; 然后, 构建双层分解模型对原始序列进行分解, 双层分解算法的应用可以获得趋势更加具有预测性的子序列, 以降低风电功率预测难度; 接着, 构建多目标策略结合改进黏菌算法优化的支持向量机(MOISMA-SVM)模型来精准预测子序列并进行相加重构。MOISMA在兼顾预测的精度和稳定性的同时对SVM参数实现了寻优, 得到风电功率预测结果; 最后, 通过MOISMA-SVM模型对预测结果的绝对误差进行进一步修正, 将误差预测结果与风电功率预测结果相加, 得到了风电功率点预测结果。通过实验对比分析, 证明了所提模型拥有最好的误差指标结果, 在两个数据集中的平均绝对误差(MAE)分别达到了0.505 7 MW和0.672 6 MW, 相比于SVM模型分别提升了98.79%和98.50%, 展现出模型的高精度结果和稳定性。根据点预测结果, 构建改进的核密度估计区间预测模型, 得到区间预测结果。两个数据集的预测区间具有较高的可靠性和较窄的区间带宽, 综合覆盖宽度准则(CWC)分别达到0.002 4和0.002 8, 能更准确地描述风电功率的波动趋势, 提高了整体模型的实用性。

关键词: 风电功率预测, 分解模型, 误差修正, 区间预测, 多目标优化