| Title |
The Prediction of Photovoltaic Power Using Regression Models Based on Weather Big-data and Sensing Data |
| Authors |
박소연(So-Yeon Park) ; 유인호(Jun-ho Bang) ; 김태형(In-ho Ryu) ; 방준호(Tae-hyeong Kim) |
| DOI |
https://doi.org/10.5370/KIEE.2019.68.12.1662 |
| Keywords |
Machine learning; Linear Regression; SVR; DNN; Predcition |
| Abstract |
In this paper, a model for predicting photovoltaic power based on collecting weather big-data and data from photovoltaic power plants is proposed with linear regression models of machining technique. The temperature, humidity, illumination, and fine dust data of photovoltaic power plants were collected and the values fused with the weather big-data were utilized as the regression model learning data. The three regression models of LR, SVR and DNN were compared and the results and accuracy of the error function were predicted by applying photovoltaic power data to each model. When using the DNN model, it was confirmed that it would have the highest accuracy from the data for predicting photovoltaic power generation. Using the designed DNN model, photovoltaic power can be predicted in any area, and accuracy can be improved according to the seasonal climate and standards of the area and the quality of comparative data. |