Prediction of Power Load Associated with Multiple Factors Based on LSTM Network

Power load forecasting is crucial for ensuring the safe and stable operation of the system. However, due to its characteristics of randomness, volatility, periodicity, and vast amounts of data, traditional forecasting methods can no longer guarantee the accuracy of predictions. Power load data typically exhibits time series and nonlinear characteristics, making LSTM an ideal choice for power load forecasting, given its exceptional capability in handling sequential data. Firstly, this paper analyzes the characteristics of factors related to power load (including local temperature, humidity, and population density of residential areas) to determine the input variables for forecasting power load. Subsequently, a deep neural network structure centered around the Long Short-Term Memory (LSTM) module is constructed for predicting power load. Finally, a vast amount of historical data on power load and related factors is collected to train and validate the constructed neural network. The experimental results demonstrate that, compared to traditional power load forecasting methods, the approach presented in this paper exhibits significant advantages in terms of prediction accuracy and reliability.