Short-Term Electrical Load Prediction Using ANN-Backpropagation

2021 ◽  
Author(s):  
Almira Hafidzanti Zuraidah ◽  
Novie Ayub Windarko ◽  
Rachma Prilian Eviningsih
Keyword(s):  
Electrical Load ◽  
Load Prediction ◽  
Short Term

scholarly journals Comparison methods of short term electrical load forecasting

2018 ◽  
Vol 218 ◽  
pp. 01002 ◽  
Author(s):  
Hartono ◽  
Arif Marifa Ahmad ◽  
M. Sadikin
Keyword(s):  
Conjugate Gradient ◽  
Load Forecasting ◽  
Data Retrieval ◽  
Bayesian Regularization ◽  
Electrical Load ◽  
Load Prediction ◽  
Short Term ◽  
Scaled Conjugate Gradient ◽  
Levenberg Marquardt ◽  
Electrical Load Forecasting

The supply of electricity that exceeds the load requirement results in the occurrence of electrical power losses. To provide the appropriate power supply to these needs, there must be a plan for the provision of electricity by making prediction or estimation of electrical load. Therefore the issue of electrical load forecasting becomes very important in the provision of efficient power. In this study, the author tries to build a model of short-term electrical load prediction using artificial neural network (ANN) with learning algorithm levenberg-marquardt (Trainlm), Bayesian regularization (Trainbr) and scaled conjugate gradient (Trainscg). Scope of research data retrieval is limited electrical load on the work area of Serang City. The results of this study show that the JST prediction of levenberg-marquardt (Trainlm) algorithm is better than the calculated prediction using Bayesian regularization (Trainbr) and scaled conjugate gradient (Trainscg) algorithms. The electric load prediction shows that the average error (Trainlm) is 3.37. Thus, it can be concluded that the electrical load prediction using the levenberg-marquardt (Trainlm) JST algorithm is more accurate than that of the Bayesian regularization (Trainbr) JST algorithm and the scaled conjugate gradient (Trainscg)

scholarly journals Electrical Load Prediction for Short Term using Support Vector Machine Techniques

2019 ◽  
Vol 9 (1S3) ◽  
pp. 111-116
Keyword(s):  
Support Vector Machine ◽  
Prediction Models ◽  
Load Forecasting ◽  
Training Data ◽  
Support Vector ◽  
Electrical Load ◽  
Load Prediction ◽  
Short Term ◽  
Training Time ◽  
Short Term Load Forecasting

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.

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