Electrical Load Forecasting using SVM Algorithm

  • et al.
N/ACitations
Citations of this article
5Readers
Mendeley users who have this article in their library.
Get full text

Abstract

Electrical load demand is variable in nature. Also, with the increase in technological development and automation, electric load demand tends to rise with time. For this, our generation facilities should be adequate 24x7 to meet the consumer’s load demand effectively. Therefore, load demand needs to be predicted or forecasted to avoid the energy crisis. In this paper, support vector machine (SVM) algorithm is explored for electric load forecasting. The live load data for the period of three months i.e., January to March, 2015, from a typical 66kV sub-station of the Punjab State Power Corporation Limited (PSPCL) for a selected site at Bhai Roopa sub-station, Bathinda, situated in the Punjab state of India, is acquired for the presented simulation study. The collected live data is divided into three categories, i.e., validation, training, and testing for the simulation study considering a SVM approach. Then, based on the environmental data input for the next 50 hours, the electric load is predicted. The obtained results from simulation were validated with the live load data of the selected site and found to be within the permissible limits. The mean square error (MSE), root-mean-square error (RMSE), mean absolute error (MAE), absolute percentage error (APE), mean absolute percentage error (MAPE) and sum of squares error (SSE) were calculated to show the effectiveness of the proposed support vector machine (SVM) algorithm based STLF. SVM is one of the effective machine learning algorithms. The errors so obtained clearly suggest that the proposed SVM algorithm gives reasonably accurate results, and is reliable for electric load forecasting.

Cite

CITATION STYLE

APA

Nijhawan*, P., Bhalla, V. K., … Gupta, J. (2020). Electrical Load Forecasting using SVM Algorithm. International Journal of Recent Technology and Engineering (IJRTE), 8(6), 4811–4816. https://doi.org/10.35940/ijrte.f9072.038620

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free