Probabilistic Wind Power Forecasting Using Optimized Deep Auto-Regressive Recurrent Neural Networks

Wind power forecasting is very crucial for power system planning and scheduling. Deep neural networks (DNNs) are widely used in forecasting applications due to their exceptional performance. However, the DNNs' architectural configuration has a significant impact on their performance, and the se...

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Bibliographic Details
Published in:IEEE transactions on industrial informatics Vol. 19; no. 3; pp. 2814 - 2825
Main Authors: Arora, Parul, Jalali, Seyed Mohammad Jafar, Ahmadian, Sajad, Panigrahi, B. K., Suganthan, P. N., Khosravi, Abbas
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.03.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Artificial neural networks
Chaos theory
Datasets
Deep auto-regressive (DeepAr)
Evolutionary algorithms
Forecasting
Genetic algorithms
Machine learning
Mathematical models
modified grasshopper optimization algorithm (MGOA)
Neural networks
neuroevolution (NE)
Optimization
Parameters
Predictive models
probabilistic forecasting
Probabilistic logic
Recurrent neural networks
Statistical analysis
Wind forecasting
Wind power
wind power (WP)
ISSN:1551-3203, 1941-0050
Online Access:Get full text
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Summary:Wind power forecasting is very crucial for power system planning and scheduling. Deep neural networks (DNNs) are widely used in forecasting applications due to their exceptional performance. However, the DNNs' architectural configuration has a significant impact on their performance, and the selection of proper hyper-parameters determines the success or failure of these models. Therefore, one of the challenging issues in DNNs is how to assess their hyper-parameter values effectively. Most of the previous researches in the literature have tuned the DNNs' hyper-parameters manually, which is a weak and time-consuming task. Using optimization/evolutionary algorithms is an effective way to obtain the optimal values of DNNs' hyper-parameters automatically. In this article, we propose a novel evolutionary algorithm that is based on the grasshopper optimization algorithm (GOA) improved by adding two evolutionary operators, opposition-based learning and chaos theory, to the optimization process. Overall, a novel probabilistic wind power forecasting model named neural GOA deep auto-regressive (NGOA-DeepAr) is proposed based on an auto-regressive recurrent neural network in which the proposed evolutionary algorithm has optimized its hyper-parameters. The performance of the proposed NGOA-DeepAr model is tested on two different datasets: One is the publicly available GEFCom-2014 dataset and the other is the Australian Energy Market Operator dataset. The prediction interval coverage probability and pinball loss for the two datasets are <inline-formula><tex-math notation="LaTeX">[0.902, 0.320]</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">[0.933, 1.4885]</tex-math></inline-formula>, respectively. According to the experimental findings, our proposed NGOA-DeepAr is much faster in learning and outperforms the benchmark DNNs and the other neuroevolutionary models.
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ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2022.3160696