6,792 research outputs found
Denoising diffusion probabilistic models for probabilistic energy forecasting
Scenario-based probabilistic forecasts have become vital for decision-makers
in handling intermittent renewable energies. This paper presents a recent
promising deep learning generative approach called denoising diffusion
probabilistic models. It is a class of latent variable models which have
recently demonstrated impressive results in the computer vision community.
However, to our knowledge, there has yet to be a demonstration that they can
generate high-quality samples of load, PV, or wind power time series, crucial
elements to face the new challenges in power systems applications. Thus, we
propose the first implementation of this model for energy forecasting using the
open data of the Global Energy Forecasting Competition 2014. The results
demonstrate this approach is competitive with other state-of-the-art deep
learning generative models, including generative adversarial networks,
variational autoencoders, and normalizing flows.Comment: Version accepted to Powertech 2023. arXiv admin note: text overlap
with arXiv:2106.09370, arXiv:2107.0103
DiffECG: A Generalized Probabilistic Diffusion Model for ECG Signals Synthesis
In recent years, deep generative models have gained attention as a promising
data augmentation solution for heart disease detection using deep learning
approaches applied to ECG signals. In this paper, we introduce a novel approach
based on denoising diffusion probabilistic models for ECG synthesis that covers
three scenarios: heartbeat generation, partial signal completion, and full
heartbeat forecasting. Our approach represents the first generalized
conditional approach for ECG synthesis, and our experimental results
demonstrate its effectiveness for various ECG-related tasks. Moreover, we show
that our approach outperforms other state-of-the-art ECG generative models and
can enhance the performance of state-of-the-art classifiers.Comment: under revie
Deep Probabilistic Time Series Forecasting using Augmented Recurrent Input for Dynamic Systems
The demand of probabilistic time series forecasting has been recently raised
in various dynamic system scenarios, for example, system identification and
prognostic and health management of machines. To this end, we combine the
advances in both deep generative models and state space model (SSM) to come up
with a novel, data-driven deep probabilistic sequence model. Specially, we
follow the popular encoder-decoder generative structure to build the recurrent
neural networks (RNN) assisted variational sequence model on an augmented
recurrent input space, which could induce rich stochastic sequence dependency.
Besides, in order to alleviate the issue of inconsistency between training and
predicting as well as improving the mining of dynamic patterns, we (i) propose
using a hybrid output as input at next time step, which brings training and
predicting into alignment; and (ii) further devise a generalized
auto-regressive strategy that encodes all the historical dependencies at
current time step. Thereafter, we first investigate the methodological
characteristics of the proposed deep probabilistic sequence model on toy cases,
and then comprehensively demonstrate the superiority of our model against
existing deep probabilistic SSM models through extensive numerical experiments
on eight system identification benchmarks from various dynamic systems.
Finally, we apply our sequence model to a real-world centrifugal compressor
sensor data forecasting problem, and again verify its outstanding performance
by quantifying the time series predictive distribution.Comment: 25 pages, 7 figures, 4 tables, preprint under revie
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