Harnessing eXplainable artificial intelligence for feature selection in time series energy forecasting: A comparative analysis of Grad-CAM and SHAP

This study investigates the efficacy of Explainable Artificial Intelligence (XAI) methods, specifically Gradient-weighted Class Activation Mapping (Grad-CAM) and Shapley Additive Explanations (SHAP), in the feature selection process for national demand forecasting. Utilising a multi-headed Convolutional Neural Network (CNN), both XAI methods exhibit capabilities in enhancing forecasting accuracy and model efficiency by identifying and eliminating irrelevant features. Comparative analysis revealed Grad-CAM’s exceptional computational efficiency in high-dimensional applications and SHAP’s superior ability in revealing features that degrade forecast accuracy. However, limitations are found in both methods, with…

• RA
  Royal Academy of Engineering
• NR
  National Research Foundation
• NN
  National Natural Science Foundation of China

  Awards: No. 62133015, 62133015