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Systematic Review on Impact of Different Irradiance Forecasting Techniques for Solar Energy Prediction
| Content Provider | MDPI |
|---|---|
| Author | Sudharshan, Konduru Naveen, C. Vishnuram, Pradeep Kasagani, Damodhara Venkata Siva Krishna Rao Nastasi, Benedetto |
| Copyright Year | 2022 |
| Description | As non-renewable energy sources are in the verge of exhaustion, the entire world turns towards renewable sources to fill its energy demand. In the near future, solar energy will be a major contributor of renewable energy, but the integration of unreliable solar energy sources directly into the grid makes the existing system complex. To reduce the complexity, a microgrid system is a better solution. Solar energy forecasting models improve the reliability of the solar plant in microgrid operations. Uncertainty in solar energy prediction is the challenge in generating reliable energy. Employing, understanding, training, and evaluating several forecasting models with available meteorological data will ensure the selection of an appropriate forecast model for any particular location. New strategies and approaches emerge day by day to increase the model accuracy, with an ultimate objective of minimizing uncertainty in forecasting. Conventional methods include a lot of differential mathematical calculations. Large data availability at solar stations make use of various Artificial Intelligence (AI) techniques for computing, forecasting, and predicting solar radiation energy. The recent evolution of ensemble and hybrid models predicts solar radiation accurately compared to all the models. This paper reviews various models in solar irradiance and power estimation which are tabulated by classification types mentioned. |
| Starting Page | 6267 |
| e-ISSN | 19961073 |
| DOI | 10.3390/en15176267 |
| Journal | Energies |
| Issue Number | 17 |
| Volume Number | 15 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-08-28 |
| Access Restriction | Open |
| Subject Keyword | Energies Industrial Engineering Solar Energy Forecast Time Series Models Hybrid Model Ensemble Learning Ai Techniques |
| Content Type | Text |
| Resource Type | Article |
