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Feedforward Error Learning Deep Neural Networks for Multivariate Deterministic Power Forecasting
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DOI：10.1109/TII.2022.3160628.png)
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摘要
En 中文
This article proposes a deep neural network (DNN) framework for multivariate deterministic power forecasting in the context of the high penetration of variable and uncertain renewable energy sources. The deep learning model is organized based on the 1-D convolutional neural network to lessen the computational burden, typical of recurrent neural network based models, and combines WaveNet and EfficientNet to improve the forecasting accuracy. Motivated by the inefficiency that all the models conduct the same tasks in the popular ensemble approach, we also designed a feedforward error learning DNN, which computes the error of the basic model separately. We further incorporated embedded and filter methods for feature selection to enhance the model visibility and the utility of the framework. Comprehensive studies on the public load and PV datasets demonstrate that the proposed framework outperforms the conventional methods in applicability, computational efficiency, and forecasting accuracy.
Keyword:
Predictive models
Forecasting
Computational modeling
Data models
Task analysis
Convolutional neural networks
Load modeling
Compound scaling
convolutional neural networks (CNN)
deterministic power forecasting
error learning
feature selection
multivariate forecasting
期刊
IF:
9.9
论文数:
8.6K
被引数:
6.0W
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