Optimize Short Term load Forcasting Anomalous Based Feed Forward Backpropagation

1st Annual Applied Science and Engineering Conference

工业技术;自然科学

Mulyadi, Y.^1 ; Abdullah, A.G.^1 ; Rohmah, K.A.^1

Electrical Power Systems Research Group, Department of Electrical Engineering Education, Indonesia University of Education, Jl. Dr. Setiabudi No. 207, Bandung

40154, Indonesia^1

关键词: Computation process;    Electrical load forecasting;    Feed-forward back propagation;    Forecasting accuracy;    Momentum constant;    Number of iterations;    Short term load forecasting;    Short term loads;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/180/1/012076/pdf
DOI  :  10.1088/1757-899X/180/1/012076

来源: IOP

【 摘 要 】

This paper contains the Short-Term Load Forecasting (STLF) using artificial neural network especially feed forward back propagation algorithm which is particularly optimized in order to getting a reduced error value result. Electrical load forecasting target is a holiday that hasn't identical pattern and different from weekday's pattern, in other words the pattern of holiday load is an anomalous. Under these conditions, the level of forecasting accuracy will be decrease. Hence we need a method that capable to reducing error value in anomalous load forecasting. Learning process of algorithm is supervised or controlled, then some parameters are arranged before performing computation process. Momentum constant a value is set at 0.8 which serve as a reference because it has the greatest converge tendency. Learning rate selection is made up to 2 decimal digits. In addition, hidden layer and input component are tested in several variation of number also. The test result leads to the conclusion that the number of hidden layer impact on the forecasting accuracy and test duration determined by the number of iterations when performing input data until it reaches the maximum of a parameter value.