Electrical Load Forecasting using SVM Algorithm
Parag Nijhawan1, Vinod Kumar Bhalla2, Manish Kumar Singla3, Jyoti Gupta4
1Parag Nijhawan, EIE Department, Thapar Institute of Engineering and Technology, Patiala India.
2Vinod Kumar Bhalla, CSE Department, Thapar Institute of Engineering and Technology, Patiala India.
3Manish Kumar Singla, EIE Department, Thapar Institute of Engineering and Technology, Patiala India.
4Jyoti Gupta, EIE Department, Thapar Institute of Engineering and Technology, Patiala India.
Manuscript received on March 12, 2020. | Revised Manuscript received on March 26, 2020. | Manuscript published on March 30, 2020. | PP: 4811-4816 | Volume-8 Issue-6, March 2020. | Retrieval Number: F9072038620/2020©BEIESP | DOI: 10.35940/ijrte.F9072.038620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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 24×7 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.
Keywords: Power System Planning, Machine Learning, Spinning Reserve, Support Vector Machine.
Scope of the Article: Algorithm Engineering.