|
| Objective | Advantage | Disadvantage | Reference |
|
| Forecast of PV | Highly versatile, made up of four different modules. High accuracy | Not easy to understand. Needs high computing power | [6] |
| Forecast of PV power | Physics inspired, and uses stochastic to increase efficiency. Uses three separate modules, two for numerical forecasting and other for AI based models. | Data collection is tougher than other methods. Solution is long and complicated. | [7, 8] |
| Solar power forecasting | Uses adaptive and linear time series. Easy to implement | Low accuracy during dusk and dawn | [9] |
| Estimate PV power | Hybrid model uses SARIMA and SVM methods. Good accuracy | Extremely complex to execute | [10] |
| Forecast PV plant output | Uses a physical model with software such as PVSyst and SAM | Deterministic forecasting is common. Fails to account for uncertainties in PV power data. High computing cost | [11, 12, 14] |
| Solar PV forecasting | Presents sate of the art PV power forecasting technique using extreme learning method | Can lead to over fitting. Uncertain performance of the model | [13, 15] |
| PV performance | Increased accuracy. Use of physical systems to get data | Expensive during setting up and can get complex | [16–18] |
| PV power forecasting | Predicted the solar irradiance using machine learning techniques, rather than PV power itself | More complexity. Did not get the PV power | [15, 20–23] |
| PV power forecasting | Increased accuracy | Uses expensive and restricted equipment | [24, 25, 28, 29] |
| PV power prediction | High accuracy. Simple model to understand | Data collection is done on hardware basis and would be expensive | [26] |
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