| Title | Multiscale LSTM-Based Deep Learning for Very-Short-Term Photovoltaic Power Generation Forecasting in Smart City Energy Management |
|---|---|
| ID_Doc | 39359 |
| Authors | Kim, D; Kwon, D; Park, L; Kim, J; Cho, S |
| Title | Multiscale LSTM-Based Deep Learning for Very-Short-Term Photovoltaic Power Generation Forecasting in Smart City Energy Management |
| Year | 2021 |
| Published | Ieee Systems Journal, 15, 1 |
| Abstract | Photovoltaic power generation forecasting (PVGF) is an attractive research topic for efficient energy management in smart city. In addition, the long short-term memory recurrent neural network (LSTM/RNN) has been actively utilized for predicting various time series tasks in recent years due to its outstanding ability to learn the feature of sequential time-series data. Although the existing forecasting models were obtained from learning the sequential PVGF data, it is observed that irregular factors made adverse effects on the forecasting results of very-short-term PVGF tasks, thus, the entire forecasting performance was deteriorated. In this regard, multiscale LSTM-based deep learning which is capable for forecasting very-short-term PVGF is proposed for efficient management. The model concatenates on two different scaled LSTM modules to overcome the deterioration that is originated from the irregular factors. Lastly, experimental results present the proposed framework can assist to forecast the tendency of PVGF amount steadily. |
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