Load Forecasting Methods

Load forecasting is a key part of power system operation and scheduling, and it is important to correctly carry out load forecasting to ensure the safe and stable operation of the power grid, optimal scheduling and economic operation. Commonly used load forecasting methods include statistical methods, artificial neural network methods and time series methods.

Statistical methods mainly rely on the statistical characteristics of historical data to predict the future load, by analyzing the periodicity, trend, seasonality and other characteristics of the historical load data, and establishing the corresponding mathematical model for prediction.

Artificial neural network methods, on the other hand, train historical data through artificial neural networks to learn the intrinsic connection between the data and use the learned knowledge to make load predictions.

Time series methods, on the other hand, are based on the principle of time series analysis, where load data is viewed as a sequence of data with temporal characteristics, which is modeled using a time series model, and the model is used to forecast future loads.

In practical applications, multiple methods are usually used for load forecasting to improve forecasting accuracy and reliability.