Grid Load Forecasting Based on Long Short-Term Memory Network

In order to build a new power system with new energy as the main goal, power load forecasting is conducive to the establishment of a safe and efficient energy system, and load forecasting plays a crucial role in the planning and scheduling of the new power system, while the traditional load forecasting method is often limited by the accuracy, and there are shortcomings such as difficulty in adapting to the fast-changing conditions, which can’t satisfy the accuracy, stability, and efficiency of load forecasting of the new power system. It cannot meet the accuracy, stability and efficiency of new power system load forecasting. Therefore, this paper adopts the Long ShortTerm Memory (LSTM) network model for power load forecasting, and processes and forecasts important events with long intervals and delays in the time series through a coherent process of data collection, data preprocessing, model training, forecast execution and post-processing, and compares it with traditional time series forecasting and exponential smoothing methods. From the algorithmic analysis, it can be concluded that the LSTM model has less relative error in prediction and more reliable prediction results.