Short-term electricity load forecasting with multi-frequency reconstruction diffusion
On the map: Diffusion models → Flow matching, time series and science
Abstract (arXiv)
Diffusion models have emerged as a powerful method in various applications. However, their application to Short-Term Electricity Load Forecasting (STELF) -- a typical scenario in energy systems -- remains largely unexplored. Considering the nonlinear and fluctuating characteristics of the load data, effectively utilizing the powerful modeling capabilities of diffusion models to enhance STELF accuracy remains a challenge. This paper proposes a novel diffusion model with multi-frequency reconstruction for STELF, referred to as the Multi-Frequency-Reconstruction-based Diffusion (MFRD) model. The MFRD model achieves accurate load forecasting through four key steps: (1) The original data is combined with the decomposed multi-frequency modes to form a new data representation; (2) The diffusion model adds noise to the new data, effectively reducing and weakening the noise in the original data; (3) The reverse process adopts a denoising network that combines Long Short-Term Memory (LSTM) and Transformer to enhance noise removal; and (4) The inference process generates the final predictions based on the trained denoising network. To validate the effectiveness of the MFRD model, we conducted experiments on two data platforms: Australian Energy Market Operator (AEMO) and Independent System Operator of New England (ISO-NE). The experimental results show that our model consistently outperforms the compared models.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Flow matching, time series and science, one of 14 directions in Diffusion models. Builds on 13 of its 59 references that are in the map. First posted on arXiv 10 Jan 2026.
Builds on
13 of 59 references.
Closest ancestors · 3
Same direction, newest first
- 2024MG-TSD: Multi-Granularity Time Series Diffusion Models with Guided Learning Process9 Mar
- 2024Diffusion-TS: Interpretable Diffusion for General Time Series Generation4 Mar
- 2021Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting28 Jan
Other references · 10
- 2017Attention Is All You Needcited by 60,361
- 2020Denoising Diffusion Probabilistic Modelscited by 21,076
- 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,169
- 2021Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecastingcited by 1,676
- 2022FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecastingcited by 1,210
- 2022HyperTime: Implicit Neural Representation for Time Seriescited by 24
- 2018Short-term Load Forecasting with Deep Residual Networkscited by 22
- 2024Short-Term Electricity-Load Forecasting by Deep Learning: A Comprehensive Surveycited by 6
- 2023DiffLoad: Uncertainty Quantification in Electrical Load Forecasting with the Diffusion Modelcited by 3
- 2022Experimental Investigation of Variational Mode Decomposition and Deep Learning for Short-Term Multi-horizon Residential Electric Load Forecastingcited by 2
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- 2024Diffusion Model-based Parameter Estimation in Dynamic Power Systems
- 2022Denoising diffusion probabilistic models for probabilistic energy forecasting
- 2022TgDLF2.0: Theory-guided deep-learning for electrical load forecasting via Transformer and transfer learning
- 2025Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios