Forecasting Short-Term Electrical Loads Using Support Vector Regression with Gaussian Kernel Functions

DOI

10.2991/978-94-6463-228-6_16How to use a DOI?

Keywords

Forecasting; Support Vector Regression; Short-Term; Electrical Load

Abstract

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.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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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  -