Comparative Study on the Precision of Photovoltaic Power Generation Prediction Based on Integrated Models
School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China
| Abstract: | Short-term photovoltaic power generation prediction is of great significance for improving the stability of power system grid connection. In order to improve the accuracy of short-term photovoltaic power generation prediction, a prediction method is proposed based on integrated models. Firstly, the quartile method is used to detect whether there are outliers in the raw data, and then multiple feature variables that affect short-term photovoltaic power generation are selected by combining feature contribution and Pearson correlation coefficient. Secondly, construct the structure of the integrated model and use the k-fold cross validation method to train the models in the primary and secondary learners. It is a new feature of the secondary learner with the results of the primary learner. Finally, the effectiveness of this method was verified using actual datasets. The simulation results show that a primary learner with high compatibility with outliers is beneficial for improving the prediction accuracy of photovoltaic power generation. The combination of two feature selection methods can more effectively extract information from the original data and improve the generalization ability of the model compared to a single feature extraction method. Moreover, the integrated model can achieve higher prediction accuracy than a single model. |
| Keywords: | Photovoltaic Power Generation; Solar Radiation Factors; Feature Engineering; Integrated Model; Cross Validation |
| DOI: | 10.57237/j.res.2023.02.007 |
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Received: 15 July 2023
Accepted: 6 September 2023
Published Online: 8 September 2023
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