mlsanityData to 14 Oct 2026

DiffLoad: Uncertainty Quantification in Electrical Load Forecasting with the Diffusion Model

arXiv 2306.01001 ↗First posted 31 May 2023cs.LG cs.AI stat.MLcited by 3

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Abstract (arXiv)

Electrical load forecasting plays a crucial role in decision-making for power systems, including unit commitment and economic dispatch. The integration of renewable energy sources and the occurrence of external events, such as the COVID-19 pandemic, have rapidly increased uncertainties in load forecasting. The uncertainties in load forecasting can be divided into two types: epistemic uncertainty and aleatoric uncertainty. Separating these types of uncertainties can help decision-makers better understand where and to what extent the uncertainty is, thereby enhancing their confidence in the following decision-making. This paper proposes a diffusion-based Seq2Seq structure to estimate epistemic uncertainty and employs the robust additive Cauchy distribution to estimate aleatoric uncertainty. Our method not only ensures the accuracy of load forecasting but also demonstrates the ability to separate the two types of uncertainties and be applicable to different levels of loads. The relevant code can be found at urlhttps://anonymous.4open.science/r/DiffLoad-4714/.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 18 of its 55 references that are in the map. First posted on arXiv 31 May 2023.

Builds on

18 of 55 references.

References · 18

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,448
  3. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,057
  4. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,926
  5. 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,352
  6. 2016Variational Inference: A Review for Statisticianscited by 1,472
  7. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,424
  8. 2019Normalizing Flows for Probabilistic Modeling and Inferencecited by 1,323
  9. 2017DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networkscited by 1,013
  10. 2019N-BEATS: Neural basis expansion analysis for interpretable time series forecastingcited by 753
  11. 2021Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecastingcited by 337
  12. 2019Diagnosing and Enhancing VAE Modelscited by 193
6 more
  1. 2019Sampling-free Epistemic Uncertainty Estimation Using Approximated Variance Propagationcited by 74
  2. 2021Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSxcited by 64
  3. 2018Bayesian Layers: A Module for Neural Network Uncertaintycited by 57
  4. 2020RobustTAD: Robust Time Series Anomaly Detection via Decomposition and Convolutional Neural Networkscited by 55
  5. 2018Understanding the Disharmony between Dropout and Batch Normalization by Variance Shiftcited by 37
  6. 2021Denoising Diffusion Gamma Modelscited by 27

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Short-term electricity load forecasting with multi-frequency reconstruction diffusioncited by 0
  2. 2026SPECTRA: State-Space Exogenous Context and Temporal-Frequency Resolution Architecture for Probabilistic Energy Forecastingcited by 0
  3. 2026ADAPT: Physics-Aware Diffusion-based World Models for Adaptive Predictive Transferable HVAC Controlcited by 0

Similar, not cited

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  1. 2024DiffPLF: A Conditional Diffusion Model for Probabilistic Forecasting of EV Charging Load
  2. 2026Quantifying Epistemic Uncertainty in Diffusion Models
  3. 2024Estimating Epistemic and Aleatoric Uncertainty with a Single Model
  4. 2025Disentangling Uncertainties by Learning Compressed Data Representation
  5. 2026Differencing the Diffusion Trajectory toward Uncertain Components for Time Series Forecasting
  6. 2025Elucidated Rolling Diffusion Models for Probabilistic Forecasting of Complex Dynamics
  7. 2025Non-stationary Diffusion For Probabilistic Time Series Forecasting
  8. 2026SynEnergy: Anomaly Semantic-Guided Diffusion for Synthetic Energy Data Generation