In:
International Journal of Recent Technology and Engineering (IJRTE), Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP, Vol. 8, No. 6 ( 2020-03-30), p. 4811-4816
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.
Type of Medium:
Online Resource
ISSN:
2277-3878
DOI:
10.35940/ijrte.2277-3878
DOI:
10.35940/ijrte.F9072.038620
Language:
Unknown
Publisher:
Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP
Publication Date:
2020
detail.hit.zdb_id:
2722057-6
