论文--中国科学院地理科学与资源研究所

论文

论文题目：	Multi-step-ahead solar irradiance modeling employing multi-frequency deep learning models and climatic data
第一作者：	Nourani Vahid， Sharghi Elnaz， Behfar Nazanin， Zhang Yongqiang
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发表年度：	2022
摘　　要：	In this paper two enhanced long-short-term memory (LSTM) models of sequenced-LSTM (SLSTM) and wavelet-LSTM (WLSTM), provided for multi-step-ahead simulation of solar irradiance of six stations, located in Iran and USA. In this respect, twenty-year recorded solar irradiance and climate data were employed. The proposed multi-frequency models serve all the capabilities of classic LSTM network and also handle its weakness in detecting and modeling multi-frequency information that often included in natural datasets. The suggested methodology improved the long-short auto-regressive term of climate-solar irradiance data by including very long frequencies of time series. The results revealed that the suggested multi-frequency LSTM methods could exceed the feed forward neural network and classic LSTM network in test phase up to 23% and 13%, respectively.
英文摘要：
刊物名称：	APPLIED ENERGY
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论文类别：	SCI