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• W4226085387 abstract "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." @default.
• W4226085387 created "2022-05-05" @default.
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• W4226085387 date "2022-06-01" @default.
• W4226085387 modified "2023-10-07" @default.
• W4226085387 title "Multi-step-ahead solar irradiance modeling employing multi-frequency deep learning models and climatic data" @default.
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• W4226085387 doi "https://doi.org/10.1016/j.apenergy.2022.119069" @default.
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