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| Title | Comparison In Power-Forecasting Methods For Geographically Distributed Pv Power Systems Using Their Previous Datasets |
|---|---|
| ID_Doc | 15125 |
| Authors | Kure T.; Miyazaki Y.; Kondoh J.; Kameda Y. |
| Year | 2020 |
| Published | 4th International Conference on Smart Grid and Smart Cities, ICSGSC 2020 |
| DOI | http://dx.doi.org/10.1109/ICSGSC50906.2020.9248569 |
| Abstract | Power generation from photovoltaic (PV) power systems is increasing from the perspective of environmental sustainability. However, the amount of PV power generated is unstable because it depends on weather conditions. For this reason, accurate prediction of PV power is required by electric power suppliers, and various forecast methods have been proposed, such as methods using machine learning. This study uses another method that predicts the short-term output of geographically distributed PV systems from the optical flow which is one of image processing methods of normalized output power distribution by applying motion compensation prediction, which is a technology for estimation of motion images. In this paper, we compare the proposed method with the persistence forecast, and evaluate the effectiveness of the proposed method. As a result, the average MAPE of the proposed method was 4.62 %, whereas that of persistence forecast was 6.4 %, which indicates that the proposed method is an effective approach for PV power forecast. © 2020 IEEE. |
| Author Keywords | component; mean absolute percentage error; normalized output power distribution; optical flow; PVpower forecast |
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