Enhancing Power Grid Load Forecasting for Ho Chi Minh City Using a Multiplayer Algorithm and Long-Short Term Memory Fusion

This paper presents an advanced power grid load forecasting approach designed specifically for Ho Chi Minh City. Our method combines a multiplayer algorithm with Long Short-Term Memory networks to improve the accuracy and efficiency of load prediction. The use of a multiplayer algorithm allows multiple models to learn collaboratively, providing a better understanding of complex load patterns. Long Short-Term Memory networks, which are proficient at capturing long-term dependencies, enhance predictive capabilities by considering historical load data. The model is trained on historical power consumption data from Ho Chi Minh City and is compared against traditional forecasting methods to evaluate its performance. The results show a significant improvement in forecasting accuracy, demonstrating the potential of the proposed approach to optimize power grid management in urban environments like Ho Chi Minh City.