Deep learning enhanced solar energy forecasting with AI-driven IoT

Zhou, Hangxia, Liu, Qian, Yan, Ke, and Du, Yang (2021) Deep learning enhanced solar energy forecasting with AI-driven IoT. Wireless Communications and Mobile Computing, 2021. 9249387.

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Short-term photovoltaic (PV) energy generation forecasting models are important, stabilizing the power integration between the PV and the smart grid for artificial intelligence- (AI-) driven internet of things (IoT) modeling of smart cities. With the recent development of AI and IoT technologies, it is possible for deep learning techniques to achieve more accurate energy generation forecasting results for the PV systems. Difficulties exist for the traditional PV energy generation forecasting method considering external feature variables, such as the seasonality. In this study, we propose a hybrid deep learning method that combines the clustering techniques, convolutional neural network (CNN), long short-term memory (LSTM), and attention mechanism with the wireless sensor network to overcome the existing difficulties of the PV energy generation forecasting problem. The overall proposed method is divided into three stages, namely, clustering, training, and forecasting. In the clustering stage, correlation analysis and self-organizing mapping are employed to select the highest relevant factors in historical data. In the training stage, a convolutional neural network, long short-term memory neural network, and attention mechanism are combined to construct a hybrid deep learning model to perform the forecasting task. In the testing stage, the most appropriate training model is selected based on the month of the testing data. The experimental results showed significantly higher prediction accuracy rates for all time intervals compared to existing methods, including traditional artificial neural networks, long short-term memory neural networks, and an algorithm combining long short-term memory neural network and attention mechanism.

Item ID: 70675
Item Type: Article (Research - C1)
ISSN: 1530-8677
Copyright Information: Copyright © 2021 Hangxia Zhou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Date Deposited: 07 Apr 2022 06:07
FoR Codes: 40 ENGINEERING > 4008 Electrical engineering > 400803 Electrical energy generation (incl. renewables, excl. photovoltaics) @ 30%
46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461103 Deep learning @ 50%
46 INFORMATION AND COMPUTING SCIENCES > 4606 Distributed computing and systems software > 460603 Cyberphysical systems and internet of things @ 20%
SEO Codes: 17 ENERGY > 1708 Renewable energy > 170804 Solar-photovoltaic energy @ 100%
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