期刊
INFORMATION
卷 11, 期 1, 页码 -出版社
MDPI
DOI: 10.3390/info11010032
关键词
short-term forecasting; solar irradiance; gated recurrent unit; attention mechanism
资金
- Zhejiang Provincial Natural Science Foundation of China [LY19F020016]
- National Natural Science Foundation of China [61850410531, 61602431, 61972156]
- research project on the 13th Five-Year Plan of higher education reform in Zhejiang Province [JG20180526]
- Program for Innovative Research Team in Science and Technology in Fujian Province University
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).
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