Multi-step-ahead solar irradiance modeling employing multi-frequency deep learning models and climatic data
文献类型：期刊论文
| 作者 | Nourani, Vahid1,2,3,4; Sharghi, Elnaz1,3; Behfar, Nazanin1,3; Zhang, Yongqiang2 |
| 刊名 | APPLIED ENERGY
 |
| 出版日期 | 2022-06-01 |
| 卷号 | 315页码:19 |
| 关键词 | Solar Irradiance Prediction LSTM Model Multi-Frequency Analysis Climate Data |
| ISSN号 | 0306-2619 |
| DOI | 10.1016/j.apenergy.2022.119069 |
| 通讯作者 | Nourani, Vahid(vahid.nourani@neu.edu.tr) |
| 英文摘要 | 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. |
| WOS关键词 | SHORT-TERM ; WEATHER FORECASTS ; EMPIRICAL-MODELS ; WAVELET ; RADIATION ; PREDICTION ; LSTM ; ANN ; DECOMPOSITION ; CONJUNCTION |
| WOS研究方向 | Energy & Fuels ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000793707400006 |
| 出版者 | ELSEVIER SCI LTD |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/176935]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Nourani, Vahid |
| 作者单位 | 1.Univ Tabriz, Ctr Excellence Hydroinformat, 29 Bahman Ave, Tabriz, Iran 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 3.Univ Tabriz, Fac Civil Engn, 29 Bahman Ave, Tabriz, Iran 4.Near East Univ, Fac Civil & Environm Eng, Nicosia, Cyprus |
| 推荐引用方式 GB/T 7714 | Nourani, Vahid,Sharghi, Elnaz,Behfar, Nazanin,et al. Multi-step-ahead solar irradiance modeling employing multi-frequency deep learning models and climatic data[J]. APPLIED ENERGY,2022,315:19. |
| APA | Nourani, Vahid,Sharghi, Elnaz,Behfar, Nazanin,&Zhang, Yongqiang.(2022).Multi-step-ahead solar irradiance modeling employing multi-frequency deep learning models and climatic data.APPLIED ENERGY,315,19. |
| MLA | Nourani, Vahid,et al."Multi-step-ahead solar irradiance modeling employing multi-frequency deep learning models and climatic data".APPLIED ENERGY 315(2022):19. |
入库方式： OAI收割
来源：地理科学与资源研究所
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