TY - JOUR
T1 - A SHAP-Guided Bi-GRU Framework for Explainable Short-Term Load Forecasting in Smart Energy Systems
AU - Wouter De Smet
AU - Liesbeth Van Dam
AU - Pieter Janssens
JF - Journal of Trustworthy and Explainable Intelligent Systems
JO - J Trustworthy Explain Intell Syst
Y1 - 2022
VL - 1
IS - 2
SP - 20
N2 - This research introduces an innovative prediction framework utilizing a parallel multi-input Bi-GRU structure coupled with a sliding window-based Multi-Input Multi-Output (MIMO) forecasting approach. The primary novelty centers on feature-specific Bi-GRU encoding, which independently processes historical demand, meteorological variables, and calendar indicators prior to data fusion, thereby empowering the network to more effectively capture temporal correlations across disparate data streams. In contrast to preceding models, our methodology distinctively merges a structured feature-specific Bi-GRU encoding mechanism with interpretability via SHAP analysis, providing both superior predictive fidelity and transparent algorithmic decision-making suitable for practical deployment within electrical grids. The introduced Bi-GRU MIMO architecture operates as the foundational model. To rigorously evaluate its performance, an attention-enhanced LSTM model is incorporated solely as a baseline for comparative analysis. Empirical validation using actual data from Singapore’s National Electricity Market System (NEMS) demonstrates that the Bi-GRU MIMO framework enhances forecasting accuracy by 1.64% and decreases MAE by 2.19% relative to traditional models, underscoring its capability to improve decision-making in smart grids.
UR - https://tipublications.com/c151496431
ER -