MATLAB-Based Autoregressive Time Series for Short-Term Electricity Load Forecasting

This project implements autoregressive time series forecasting for short-term electricity load using MATLAB, which has been successfully applied in practical engineering scenarios. The algorithm achieves high accuracy and strong stability through systematic modeling of historical load patterns using autoregressive (AR) components. It can effectively predict short-term load demands for power systems, providing critical reference data for power grid dispatch operations. The implementation typically involves key MATLAB functions such as arima() for model specification, estimate() for parameter calibration, and forecast() for generating predictions. The algorithm processes historical load data to identify optimal lag structures and coefficients that minimize prediction errors. Beyond electricity load forecasting, this algorithm can be extended to time series prediction in other domains such as financial market price forecasting and weather prediction. Therefore, this methodology demonstrates broad application prospects in practical implementations, potentially offering more accurate and reliable forecasting results across various industries, thereby contributing to economic development and social progress.