Open AccessElectrical Load Prediction for Short Term using Support Vector Machine Techniques
Author(s) -
Kartheek Vankadara,
I. Jacob Raglend
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.a1023.1291s319
Subject(s) - support vector machine , mean squared error , term (time) , computer science , mean absolute percentage error , electrical load , scheduling (production processes) , artificial intelligence , machine learning , data mining , artificial neural network , power (physics) , statistics , mathematics , mathematical optimization , physics , quantum mechanics
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.