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Open AccessArticle

Short-Term Solar Irradiance Forecasting Based on a Hybrid Deep Learning Methodology

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Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province, College of Information Engineering, China Jiliang University, Hangzhou 310018, China
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School of Computer Engineering, Weifang University, Weifang 261061, China
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Zhejiang Huayun Information Technology Co., Ltd., Hangzhou 310008, China
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Nanhu College, Jiaxing University, Jiaxing 314001, China
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Fujian Province University Key Laboratory of Computational Science, School of Mathematical Sciences, Huaqiao University, Quanzhou 362021, China
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Author to whom correspondence should be addressed.
Information 2020, 11(1), 32; https://doi.org/10.3390/info11010032
Received: 19 December 2019 / Revised: 1 January 2020 / Accepted: 2 January 2020 / Published: 6 January 2020
(This article belongs to the Special Issue Machine Learning on Scientific Data and Information)
Accurate prediction of solar irradiance is beneficial in reducing energy waste associated with photovoltaic power plants, preventing system damage caused by the severe fluctuation of solar irradiance, and stationarizing the power output integration between different power grids. Considering the randomness and multiple dimension of weather data, a hybrid deep learning model that combines a gated recurrent unit (GRU) neural network and an attention mechanism is proposed forecasting the solar irradiance changes in four different seasons. In the first step, the Inception neural network and ResNet are designed to extract features from the original dataset. Secondly, the extracted features are inputted into the recurrent neural network (RNN) network for model training. Experimental results show that the proposed hybrid deep learning model accurately predicts solar irradiance changes in a short-term manner. In addition, the forecasting performance of the model is better than traditional deep learning models (such as long short term memory and GRU). View Full-Text
Keywords: short-term forecasting; solar irradiance; gated recurrent unit; attention mechanism short-term forecasting; solar irradiance; gated recurrent unit; attention mechanism
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Yan, K.; Shen, H.; Wang, L.; Zhou, H.; Xu, M.; Mo, Y. Short-Term Solar Irradiance Forecasting Based on a Hybrid Deep Learning Methodology. Information 2020, 11, 32.

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