Online Compression Reconfiguration-Based Load Forecasting Method for Distribution Grid Power System

Abstract

The current conventional power system load forecasting method mainly outputs forecasting results by constructing a time model, which leads to poor forecasting results due to the lack of effective extraction of feature data of load signals. In this regard, an online compression and reconstruction-based load forecasting method for distribution network power systems is proposed. By introducing the concept of particle swarm ensemble, the discrete situation of power load signal data particles is characterized, and data normalization is carried out, and the load signal data is compressed and reconstructed. The maximum information coefficient is calculated and the load data features are extracted by combining the influencing factors, and finally a hybrid prediction model is constructed and the model is solved. In the experiments, the designed method is verified for its prediction effect. The experimental results show that the designed method has a good fit between the prediction results and the actual load curve, and has a good prediction performance.