华南理工大学学报（自然科学版）2026，Vol.54Issue(1)：42-52,11.DOI:10.12141/j.issn.1000-565X.250024
基于改进Informer的商业建筑短期用电负荷多步预测
Short-Term Power Load Multi-Step Forecasting for Commercial Building Based on Improved Informer
摘要
Abstract
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 fur-ther 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 vali-dation 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.关键词
商业建筑用电负荷预测/频率增强通道注意力机制/Informer/麻雀优化算法Key words
power load forecasting for commercial buildings/frequency enhanced channel attention mechanism/Informer/sparrow search algorithm分类
建筑与水利引用本文复制引用
周璇,李可昕,郭子轩,俞祝良,闫军威,蔡盼盼..基于改进Informer的商业建筑短期用电负荷多步预测[J].华南理工大学学报（自然科学版）,2026,54(1):42-52,11.基金项目
广东省自然科学基金项目(2022A1515011128)Supported by the Natural Science Foundation of Guangdong Province(2022A1515011128) （2022A1515011128）
