An Enhanced Electric Power Load Forecasting Model Considering Data Feature Extraction

This study introduces an advanced load forecasting model that integrates feature engineering with Fourier and Wavelet Transforms, PCA-based dimensionality reduction, and an Autoformer ensemble strategy. The approach significantly enhances the precision of electric load predictions by enriching data representation and optimizing model performance. Tested on the UCI Electricity Load Diagrams 2011-2014 dataset, the model demonstrates an improvement in forecasting accuracy over traditional methods. The findings highlight the importance of sophisticated data preprocessing and ensemble techniques in the realm of load forecasting, offering insights pivotal for future research in energy management and planning.