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.