Wavelet-Enhanced Neural ODE and Graph Attention for Interpretable Energy Forecasting

TL;DR

This paper presents a novel neural framework combining Neural ODEs, graph attention, wavelet transformations, and adaptive frequency learning to improve energy demand forecasting accuracy and interpretability across diverse datasets.

Contribution

It introduces an integrated model that captures multi-scale temporal dynamics and structural patterns, advancing energy forecasting methods with enhanced interpretability.

Findings

Outperforms state-of-the-art baselines on multiple datasets

Effectively captures multi-scale temporal dynamics

Provides interpretable insights via SHAP analysis

Abstract

Accurate forecasting of energy demand and supply is critical for optimizing sustainable energy systems, yet it is challenged by the variability of renewable sources and dynamic consumption patterns. This paper introduces a neural framework that integrates continuous-time Neural Ordinary Differential Equations (Neural ODEs), graph attention, multi-resolution wavelet transformations, and adaptive learning of frequencies to address the issues of time series prediction. The model employs a robust ODE solver, using the Runge-Kutta method, paired with graph-based attention and residual connections to better understand both structural and temporal patterns. Through wavelet-based feature extraction and adaptive frequency modulation, it adeptly captures and models diverse, multi-scale temporal dynamics. When evaluated across seven diverse datasets: ETTh1, ETTh2, ETTm1, ETTm2 (electricity transformer temperature), and Waste, Solar, and Hydro (renewable energy), this architecture consistently outperforms state-of-the-art baselines in various forecasting metrics, proving its robustness in capturing complex temporal dependencies. Furthermore, the model enhances interpretability through SHAP analysis, making it suitable for sustainable energy applications.