| Title |
Multiple Linear Regression Analysis of PV Power Forecasting for Evaluation and Selection of Suitable PV Sites |
| Authors |
Heo, Jae ; Park, Bumsoo ; Kim, Byungil ; Han, SangUk |
| DOI |
http://dx.doi.org/10.6106/KJCEM.2019.20.6.126 |
| Keywords |
Multiple Regression Analysis; Photovoltaic Power Generation; Meteorological Factors; Solar Energy |
| Abstract |
The estimation of available solar energy at particular locations is critical to find and assess suitable locations of PV sites. The amount of PV power generation is however affected by various geographical factors (e.g., weather), which may make it difficult to identify the complex relationship between affecting factors and power outputs and to apply findings from one study to another in different locations. This study thus undertakes a regression analysis using data collected from 172 PV plants spatially distributed in Korea to identify critical weather conditions and estimate the potential power generation of PV systems. Such data also include solar radiation, precipitation, fine dust, humidity, temperature, cloud amount, sunshine duration, and wind speed. The estimated PV power generation is then compared to the actual PV power generation to evaluate prediction performance. As a result, the proposed model achieves a MAPE of 11.696(%) and an R-squred of 0.979. It is also found that the variables, excluding humidity, are all statistically significant in predicting the efficiency of PV power generation. According, this study may facilitate the understanding of what weather conditions can be considered and the estimation of PV power generation for evaluating and determining suitable locations of PV facilities. |