| Title |
Development of SVR based Short-term Load Forecasting Algorithm |
| Authors |
오병찬(Byeong-Chan Oh) ; 김성열(Sung-Yul Kim) |
| DOI |
https://doi.org/10.5370/KIEEP.2019.68.2.095 |
| Keywords |
Load forecasting ; Machine Learning ; Support Vector Regression |
| Abstract |
In recent years, precise power demand has become more important due to the constant increase in demand for electricity and abnormal weather conditions. Due to the abnormal weather conditions such as heat wave during summer season, electric power demand forecast error is increasing due to increase of heating and cooling load and this leads to economic losses due to the lowering of the stability of the power system and the increase in the cost of additional power purchase. In this paper, we propose a short-term power demand prediction algorithm based on Support Vector Regression which is an artificial intelligence technique. We implemented a short-term prediction model based on public data through correlation analysis such as temperature, time, and past electricity demand. In order to analyze the model performance, we analyzed the kernel function and slack variable value which are SVR parameter values. Finally, cross validation was performed to solve the overfitting problem. |