Forecasting Short-Term Electrical Loads Using Support Vector Regression with Gaussian Kernel Functions
- 10.2991/978-94-6463-228-6_16How to use a DOI?
- Forecasting; Support Vector Regression; Short-Term; Electrical Load
Power outages that sometimes still occur cause disruption of economic activity around the area, especially the city of Bau-bau, it is necessary to forecast the electrical load in order to determine the characteristics of the electrical load in the area. Short-term electrical load forecasting is used to evaluate the performance of power plants related to loading scheduling and delivery. However, increasing the accuracy of electrical load forecasting has become a fundamental issue in the development of electric power systems. Because the nonlinear condition of the electrical load data affects the accuracy of the electrical load forecasting. Modeling using support vector regression (SVR) to solve nonlinear electrical load forecasting problems is very well used for conditions of lack of information and limited. The proposed SVR modeling uses the Gaussian kernel function and uses historical electrical load data as input data and target data. The criteria for short-term electrical load forecasting accuracy can be determined by calculating the Mean Square Error (MSE). The smaller the MSE value, the better the level of accuracy for forecasting electrical loads. Short-term electrical load forecasting using the Support Vector Regression kernel function Gaussian (SVR-Gaussian) gives very good forecasting results with a small MSE level. Thus, the proposed SVR model has the feasibility of forecasting short-term electrical loads in the city of Bau-bau.
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Cite this article
TY - CONF AU - La Ode Muhammad Yusuf AU - Susatyo Handoko AU - Iwan Setiawan PY - 2023 DA - 2023/08/22 TI - Forecasting Short-Term Electrical Loads Using Support Vector Regression with Gaussian Kernel Functions BT - Proceedings of the 4th International Seminar on Science and Technology (ISST 2022) PB - Atlantis Press SP - 131 EP - 142 SN - 2352-541X UR - https://doi.org/10.2991/978-94-6463-228-6_16 DO - 10.2991/978-94-6463-228-6_16 ID - Yusuf2023 ER -