Open AccessNeural Nework-Based Time Series Methods for Load Forecasting
Author(s) -
Girraj Singh,
Aseem Chandel,
D. S. Chauhan
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.a2942.059120
Subject(s) - nonlinear autoregressive exogenous model , autoregressive model , series (stratigraphy) , computer science , time series , artificial neural network , electric power system , nonlinear system , autoregressive integrated moving average , power (physics) , star model , control theory (sociology) , mathematical optimization , econometrics , artificial intelligence , machine learning , mathematics , control (management) , paleontology , physics , quantum mechanics , biology
Load forecast plays an important role in power system operation and control. Significant contribution in power system economics may also be observed. Many decisions of the power system depend on the future load demand. The accuracy of STLF is necessary for the optimal and economical operation of the power systems. This paper presents a new approach to STLF. In this paper, time series methods are presented on the basis of neural networks. The time series methods are included autoregressive, nonlinear autoregressive, and non-linear autoregressive with external inputs (narx). The comparative results are presented with the ANN. In this paper, the narx method gives more efficient and accurate results than other methods.