商业建筑短期用电负荷多步预测是城市有序用电和虚拟电厂调度的关键环节。商业建筑用电负荷时间序列具有强随机性、非平稳、非线性等特点，针对传统的迭代式多步用电负荷预测方法存在误差累积效应影响预测精度的问题，提出一种基于频率增强通道注意力机制（FECAM）—麻雀优化算法（SSA）—Informer的短期用电负荷多步预测方法。该方法在Informer编码器输出时域特征的基础上，采用FECAM对各特征通道间的频率依赖性进行自适应建模，进一步提取多维输入序列的频域特征，生成式解码器利用融合的时、频域信息直接输出未来多步用电负荷序列。此外，由于改进Informer超参数设置缺乏理论依据，使用SSA寻优学习率、批处理大小、全连接维度和失活率的最佳组合。以广州某商业建筑全年用电负荷数据作为实际算例，结果表明，与其他深度学习模型相比，所提模型在不同预测步长（48、96、288、480、672步）下的预测精度显著提升，具有更优的短期用电负荷多步预测性能。

Short-term power load multi-step forecasting for commercial buildings plays a pivotal role in urban orderly power consumption and virtual power plant scheduling. The power load time series in commercial buildings is characterized by strong stochasticity, non-stationarity, and nonlinearity, and traditional iterative multi-step power load forecasting strategy suffers from error accumulation effects that degrade prediction accuracy, a short-term power load multi-step forecasting method based on Frequency Enhanced Channel Attention Mechanism (FECAM)-Sparrow Search Algorithm (SSA)-Informer is proposed. Based on the time-domain features output by the Informer encoder, the method uses FECAM to adaptively model the frequency dependence between feature channels, and further extractings the frequency-domain features of multi-dimensional input sequences. The decoder then integrates both time-frequency domain information to directly generate future multi-step load sequences. Furthermore, due to the lack of theoretical basis for the improved Informer hyperparameter settings, the SSA is used to optimize model hyperparameters such as learning rate, batch size, fully connected dimensions, and dropout rate. Experimental validation using annual load data from a commercial building in Guangzhou demonstrates that, compared with other deep learning models, the proposed model significantly improved prediction accuracy across varying forecast horizons (steps of 48, 96, 288, 480 and 672), exhibiting superior performance in short-term power load multi-step forecasting.