Probabilistic forecasting of renewable energy and electricity demand using Graph-based Denoising Diffusion Probabilistic Model

Abstract

Renewable energy production and the balance between production and demand have become increasingly crucial in modern power systems, necessitating accurate forecasting. Traditional deterministic methods fail to capture the inherent uncertainties associated with intermittent renewable sources and fluctuating demand patterns. This paper proposes a novel denoising diffusion method for multivariate time series probabilistic forecasting that explicitly models the interdependencies between variables through graph modeling. Our framework employs a parallel feature extraction module that simultaneously captures temporal dynamics and spatial correlations, enabling improved forecasting accuracy. Through extensive evaluation on two real-world datasets focused on renewable energy and electricity demand, we demonstrate that our approach achieves state-of-the-art performance in probabilistic energy time series forecasting tasks. By explicitly modeling variable interdependencies and incorporating temporal information, our method provides reliable probabilistic forecasts, crucial for effective decision-making and resource allocation in the energy sector. Extensive experiments validate that our proposed method reduces the Continuous Ranked Probability Score (CRPS) by 2.1%–70.9%, Mean Absolute Error (MAE) by 4.4%–52.2%, and Root Mean Squared Error (RMSE) by 7.9%–53.4% over existing methods on two real-world datasets. • Novel Forecasting Method : Proposes the Graph-based Denoising Diffusion Probabilistic Model (G-DDPM) for multivariate time series forecasting, explicitly modeling interdependencies between variables using graph techniques. • Enhanced Feature Extraction : Incorporates a parallel feature extraction module to capture both temporal dynamics and spatial correlations, significantly improving forecasting accuracy. • Probabilistic Forecasting Performance : Demonstrates state-of-the-art performance in probabilistic forecasting tasks, validated through extensive evaluation on real-world renewable energy and electricity demand datasets. • Uncertainty Quantification : Provides reliable probabilistic forecasts that effectively quantify uncertainties, crucial for decision-making and resource allocation in the energy sector. • Comprehensive Evaluation : Extensively validated on two real-world datasets, showcasing its applicability and robustness in handling diverse variables originating from renewable energy sources and electricity demand patterns.

Topics & Concepts