Short-term power load forecasting method based on CNN-SAEDN-Res
- URL: http://arxiv.org/abs/2309.07140v1
- Date: Sat, 2 Sep 2023 11:36:50 GMT
- Title: Short-term power load forecasting method based on CNN-SAEDN-Res
- Authors: Yang Cui, Han Zhu, Yijian Wang, Lu Zhang, Yang Li
- Abstract summary: This paper presents a short-term load forecasting method based on convolutional neural network (CNN), self-attention encoder-decoder network (SAEDN) and residual-refinement (Res)
The proposed method has advantages in terms of prediction accuracy and prediction stability.
- Score: 13.661089157478626
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In deep learning, the load data with non-temporal factors are difficult to
process by sequence models. This problem results in insufficient precision of
the prediction. Therefore, a short-term load forecasting method based on
convolutional neural network (CNN), self-attention encoder-decoder network
(SAEDN) and residual-refinement (Res) is proposed. In this method, feature
extraction module is composed of a two-dimensional convolutional neural
network, which is used to mine the local correlation between data and obtain
high-dimensional data features. The initial load fore-casting module consists
of a self-attention encoder-decoder network and a feedforward neural network
(FFN). The module utilizes self-attention mechanisms to encode high-dimensional
features. This operation can obtain the global correlation between data.
Therefore, the model is able to retain important information based on the
coupling relationship between the data in data mixed with non-time series
factors. Then, self-attention decoding is per-formed and the feedforward neural
network is used to regression initial load. This paper introduces the residual
mechanism to build the load optimization module. The module generates residual
load values to optimize the initial load. The simulation results show that the
proposed load forecasting method has advantages in terms of prediction accuracy
and prediction stability.
Related papers
- Assessing Neural Network Representations During Training Using
Noise-Resilient Diffusion Spectral Entropy [55.014926694758195]
Entropy and mutual information in neural networks provide rich information on the learning process.
We leverage data geometry to access the underlying manifold and reliably compute these information-theoretic measures.
We show that they form noise-resistant measures of intrinsic dimensionality and relationship strength in high-dimensional simulated data.
arXiv Detail & Related papers (2023-12-04T01:32:42Z) - Deep Learning for Day Forecasts from Sparse Observations [60.041805328514876]
Deep neural networks offer an alternative paradigm for modeling weather conditions.
MetNet-3 learns from both dense and sparse data sensors and makes predictions up to 24 hours ahead for precipitation, wind, temperature and dew point.
MetNet-3 has a high temporal and spatial resolution, respectively, up to 2 minutes and 1 km as well as a low operational latency.
arXiv Detail & Related papers (2023-06-06T07:07:54Z) - Continuous time recurrent neural networks: overview and application to
forecasting blood glucose in the intensive care unit [56.801856519460465]
Continuous time autoregressive recurrent neural networks (CTRNNs) are a deep learning model that account for irregular observations.
We demonstrate the application of these models to probabilistic forecasting of blood glucose in a critical care setting.
arXiv Detail & Related papers (2023-04-14T09:39:06Z) - Hybrid machine-learned homogenization: Bayesian data mining and
convolutional neural networks [0.0]
This study aims to improve the machine learned prediction by developing novel feature descriptors.
The iterative development of feature descriptors resulted in 37 novel features, being able to reduce the prediction error by roughly one third.
A combination of the feature based approach and the convolutional neural network leads to a hybrid neural network.
arXiv Detail & Related papers (2023-02-24T09:59:29Z) - Learning to Learn with Generative Models of Neural Network Checkpoints [71.06722933442956]
We construct a dataset of neural network checkpoints and train a generative model on the parameters.
We find that our approach successfully generates parameters for a wide range of loss prompts.
We apply our method to different neural network architectures and tasks in supervised and reinforcement learning.
arXiv Detail & Related papers (2022-09-26T17:59:58Z) - An advanced spatio-temporal convolutional recurrent neural network for
storm surge predictions [73.4962254843935]
We study the capability of artificial neural network models to emulate storm surge based on the storm track/size/intensity history.
This study presents a neural network model that can predict storm surge, informed by a database of synthetic storm simulations.
arXiv Detail & Related papers (2022-04-18T23:42:18Z) - Parameter estimation for WMTI-Watson model of white matter using
encoder-decoder recurrent neural network [0.0]
In this study, we evaluate the performance of NLLS, the RNN-based method and a multilayer perceptron (MLP) on datasets rat and human brain.
We showed that the proposed RNN-based fitting approach had the advantage of highly reduced computation time over NLLS.
arXiv Detail & Related papers (2022-03-01T16:33:15Z) - Emulating Spatio-Temporal Realizations of Three-Dimensional Isotropic
Turbulence via Deep Sequence Learning Models [24.025975236316842]
We use a data-driven approach to model a three-dimensional turbulent flow using cutting-edge Deep Learning techniques.
The accuracy of the model is assessed using statistical and physics-based metrics.
arXiv Detail & Related papers (2021-12-07T03:33:39Z) - Dynamic Time Warping as a New Evaluation for Dst Forecast with Machine
Learning [0.0]
We train a neural network to make a forecast of the disturbance storm time index at origin time $t$ with a forecasting horizon of 1 up to 6 hours.
Inspection of the model's results with the correlation coefficient and RMSE indicated a performance comparable to the latest publications.
A new method is proposed to measure whether two time series are shifted in time with respect to each other.
arXiv Detail & Related papers (2020-06-08T15:14:13Z) - Error-feedback stochastic modeling strategy for time series forecasting
with convolutional neural networks [11.162185201961174]
We propose a novel Error-feedback Modeling (ESM) strategy to construct a random Convolutional Network (ESM-CNN) Neural time series forecasting task.
The proposed ESM-CNN not only outperforms the state-of-art random neural networks, but also exhibits stronger predictive power and less computing overhead in comparison to trained state-of-art deep neural network models.
arXiv Detail & Related papers (2020-02-03T13:30:29Z) - Model Fusion via Optimal Transport [64.13185244219353]
We present a layer-wise model fusion algorithm for neural networks.
We show that this can successfully yield "one-shot" knowledge transfer between neural networks trained on heterogeneous non-i.i.d. data.
arXiv Detail & Related papers (2019-10-12T22:07:15Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.