针对船舶电力负荷数据预测时存在的实时性差、存储数据量小且质量低等问题，本文提出一种结合数据插补、小波阈值去噪与概率稀疏自注意力（ProbSparse Self-Attention）机制的新型短期负荷预测方法。首先在数据预处理阶段，在不影响原始数据特征及趋势的前提下通过插值填补缺失数据，同时扩充数据库以满足模型训练要求；同时考虑到原始船舶电力负荷数据可能存在噪声干扰等问题，为减小其对模型预测效果的影响，对原信号采用了小波阈值去噪处理的方法来改善数据质量。其次在预测模型中通过引入概率稀疏自注意力机制，在有效捕获时序电力数据中的依赖关系和重要特征的同时降低内存资源占用，减小模型复杂度，满足船舶电力负荷预测实时性要求，实现预测精度与效率双优化。在与其他模型的对比实验中，本文模型在均方根误差和平均绝对百分比误差两种指标上平均分别降低了13.1%、18.6%，效率平均提高24.0%以上，结果表明该方法在船舶电力负荷数据预测模型准确度及效率上有着明显优势。

Aiming at the problems of poor real-time, small storage data and low quality of ship power load data prediction, this paper proposes a new short-term load prediction method that combines data interpolation, wavelet threshold denoising and ProbSparse Self-Attention mechanism. Firstly, in data preprocessing stage, the database is expanded by interpolation to meet the model training requirements without affecting the characteristics and trends of the original data; at the same time, taking into account noise disturbance in the original ship power load data, in order to reduce its impact on the model prediction effect, a new wavelet threshold denoising method is adopted to process the original signals and to improve the quality of the data. Secondly, by introducing probabilistic sparse self-attention mechanism in the forecasting model. While effectively capturing the dependencies and important features in the time series power data, it reduces the memory resource consumption and model complexity. It meets the real-time requirements of ship power load forecasting and realizes the double optimization of forecasting accuracy and efficiency. In the comparison experiments with other models, this paper's model reduces at least 13.1% and 18.6% on average in the two indicators of root mean square error and average absolute percentage error, respectively, and improves the efficiency by more than 24.0% on average, and the results show that the method has obvious advantages in the accuracy and efficiency of the ship power load data forecasting model.