基于xLSTM的大型水资源配置工程多区域电力消耗预测算法OA

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.