Integrating Climate Prediction into Smart Grid Load Management Using Attention-LSTM

As global climate change intensifies, the impact of meteorological conditions on power systems is becoming increasingly significant. Climatic factors such as temperature increase, precipitation fluctuation and wind speed change have far-reaching impacts on power demand, especially in extreme weather conditions, where power load fluctuations are more drastic. In order to improve the scheduling efficiency and stability of the power system, this study proposes a climate prediction method based on the Attention-LSTM model and explores the application of this method in smart grid load forecasting. It is shown that accurate climate prediction can help the power grid identify load peaks in advance, optimize power dispatch, reduce energy waste, and improve the flexibility and reliability of the power grid. In addition, climate prediction can optimize the dispatch of renewable energy sources, address the volatility of wind and solar power generation, and provide more stable power support to the grid. This study provides theoretical basis and technical support for load forecasting and dispatch optimization of smart grid in the context of climate change.