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AE-LSTM Based Deep Learning Model for Degradation Rate Influenced Energy Estimation of a PV System
| Content Provider | MDPI |
|---|---|
| Author | Aslam, Muhammad Lee, Jae-Myeong Altaha, Mustafa Raed Lee, Seung-Jae Hong, Sugwon |
| Copyright Year | 2020 |
| Description | With the increase in penetration of photovoltaics (PV) into the power system, the correct prediction of return on investment requires accurate prediction of decrease in power output over time. Degradation rates and corresponding degraded energy estimation must be known in order to predict power delivery accurately. Solar radiation plays a key role in long-term solar energy predictions. A combination of auto-encoder and long short-term memory (AE-LSTM) based deep learning approach is adopted for long-term solar radiation forecasting. First, the auto-encoder (AE) is trained for the feature extraction, and then fine-tuning with long short-term memory (LSTM) is done to get the final prediction. The input data consist of clear sky global horizontal irradiance (GHI) and historical solar radiation. After forecasting the solar radiation for three years, the corresponding degradation rate (DR) influenced energy potentials of an a-Si PV system is estimated. The estimated energy is useful economically for planning and installation of energy systems like microgrids, etc. The method of solar radiation forecasting and DR influenced energy estimation is compared with the traditional methods to show the efficiency of the proposed method. |
| Starting Page | 4373 |
| e-ISSN | 19961073 |
| DOI | 10.3390/en13174373 |
| Journal | Energies |
| Issue Number | 17 |
| Volume Number | 13 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2020-08-24 |
| Access Restriction | Open |
| Subject Keyword | Energies Industrial Engineering Auto-encoder Lstm Deep Learning Machine Learning Solar Radiation Forecasting Pv Energy Estimation Degradation Rate |
| Content Type | Text |
| Resource Type | Article |
