重庆大学学报2026，Vol.49Issue(6)：103-116,14.DOI:10.11835/j.issn.1000-582X.2026.06.010
基于xLSTM的大型水资源配置工程多区域电力消耗预测算法
Multi-regional power consumption forecasting in large-scale construction projects based on xLSTM
摘要
Abstract
With the continuous expansion of water resource allocation projects,accurate electricity consumption forecasting is crucial for energy conservation,cost control,and construction efficiency.Traditional forecasting methods,such as long short-term memory(LSTM)networks and Transformers,often struggle to capture both short-term and long-term dependencies in complex time-series data.To address this challenge,this paper proposes an xLSTM(extended long Short-term memory)model for multi-regional power consumption forecasting.The xLSTM model combines the short-term dependency modeling capability of sLSTM with the long-term dependency learning capacity of mLSTM,enabling effective analysis of power consumption data across multiple regions while considering temporal correlations among regions.Experimental results show that xLSTM achieve superior predictive performance,with a mean square error(MSE)of 0.0030 and a mean absolute error(MAE)of 0.035,outperforming competing models.The proposed model provides effective technical support for precise electricity demand forecasting and offers practical value for decision-making and intelligent scheduling management in large-scale water resource allocation projects.关键词
xLSTM/电力消耗预测/大型水资源配置工程/多区域预测Key words
xLSTM/electricity consumption forecasting/large-scale water allocation projects/multi-regional prediction分类
信息技术与安全科学引用本文复制引用
王兵,杨青山,蒋有高,徐献韬,王楷..基于xLSTM的大型水资源配置工程多区域电力消耗预测算法[J].重庆大学学报,2026,49(6):103-116,14.基金项目
重庆市技术创新与应用发展专项重点项目(CSTB2022TIAD-KPX0127).Supported by Chongqing Key Project of Technological Innovation and Application Development(CSTB2022TIAD-KPX0127). （CSTB2022TIAD-KPX0127）
