Solar Power Generation Prediction Based on Artificial Neural Network
| Autor: | Yan-Wen Wang, 王衍文 |
|---|---|
| Rok vydání: | 2018 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 106 The use of renewable energy has been actively promoted by the government in Taiwan. For example, the goal of “non-nuclear homeland” is expected to be achieved by 2025. However, the solar system installation capacity remains low, due to the unstable power output caused by unpredictable weather conditions, such as cloudy and rainy days. This study proposes a method for predicting power generation using photovoltaic systems. Various environmental parameters and satellite cloud images serve as the inputs for the prediction model. Important parameters are selected by dimensionality reduction techniques, and the filtered parameters are put into an Artificial Neural Network model to train the model. Both a long-term model and a short-term model for solar irradiance prediction are established. The solar irradiance is predicted every 10 min. Such a small prediction interval is different from the interval used in other studies. The longer the prediction interval, the more accurate the prediction results. However, the solar irradiance variation is largely ignored, if a longer prediction interval is adopted. Thus, this study uses a short interval to predict solar irradiance. The two models deal with two weather conditions: sunny and cloudy days. For sunny days, the long-term prediction error is 139.92 W / m2, and the short-term prediction error is 43.02 W / m2. For cloudy days, the long-term prediction error is 103.28 W/m2, and the short-term prediction is 36.99 W/m2. Furthermore, a regression model is used to analyze the relationship between the solar irradiance prediction results and historical power generation data (i.e., current, voltage, power, and solar irradiance), so the future solar power generation can be predicted. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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