Open AccessPrediction the data consumption for power demands by elman neural network
Author(s) -
Lafta Ismael,
Ammar Issa Ismael
Publication year - 2019
Publication title -
international journal of power electronics and drive systems/international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
eISSN - 2722-2578
pISSN - 2722-256X
DOI - 10.11591/ijece.v9i5.pp4003-4009
Subject(s) - artificial neural network , computer science , perceptron , power (physics) , power consumption , time domain , demand forecasting , electric power system , power network , domain (mathematical analysis) , artificial intelligence , operations research , engineering , mathematics , mathematical analysis , physics , quantum mechanics , computer vision
The load forecasting consider as part important in power system operation. The exact prediction for power demand is important for planning how much need extra power generation to cover extra load to keep without happen shutdown. Neural networks stay frequently designed for modeling dynamic processes. The Multi-Layer Perceptron (MLP) with Radial Basis Functions (RBF) network is static approximations used fewer frequently in the discrete-time domain. In this paper proposed predict method for daily peak load by Elman Neural Network (ENN) with using data power demand for 2 years collected from National Control Center (NCC) and comparing the result. The result show the proposal is evaluated and followed the power demand.