Electrical Load Prediction for Short Term using Support Vector Machine Techniques
Kartheek Vankadara1, I. Jacob Raglend2
1Kartheek Vankadara, Department of Electrical Engineering, Vellore Institute of Technology, Vellore (Tamil Nadu), India.
2I. Jacob Raglend, Department of Electrical Engineering, Vellore Institute of Technology, Vellore (Tamil Nadu), India.
Manuscript received on 14 December 2019 | Revised Manuscript received on 22 December 2019 | Manuscript Published on 31 December 2019 | PP: 111-116 | Volume-9 Issue-1S3 December 2019 | Retrieval Number: A10231291S319/19©BEIESP | DOI: 10.35940/ijeat.A1023.1291S319
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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: The electrical load prediction during an interval of a week or a day plays an important role for scheduling and controlling operations of any power system. The techniques which are presently being used and are used for Short Term Load Forecasting (STLF) by utilizing various prediction models try for the performance improvement. The prediction models and their performance mainly depend upon the training data and its quality. The different forecasting approaches using Support Vector Machine (SVM) depending on several performance indices has been discussed. The accuracy of the forecasting approaches is measured by Mean Absolute Error (MAE), Root Mean Square Error (RMSE), prediction speed and training time. The approach with least RMSE reveals as the best among the SVM methods for short term load forecasting.
Keywords: Load Forecasting, Machine Learning, RMSE, Support Vector Machine.
Scope of the Article: Machine Learning