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

Article · Amir Miraki, Pekka Parviainen, Reza Arghandeh

- Archive id: doi:10.1016/j.egyai.2024.100459
- Published: 2024-12-12
- Venue: Energy and AI
- DOI: https://doi.org/10.1016/j.egyai.2024.100459
- Citations: 44
- Source URL: https://doi.org/10.1016/j.egyai.2024.100459
- Provenance: openalex

## 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.

_Abstract reproduced under CC-BY (publisher license, via OpenAlex)._

Topics: [Generative & Multimodal Models](https://space-kitty.com/topics/generative-multimodal)

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