Neural Nework-Based Time Series Methods for Load Forecasting | Zendy

Girraj Singh | Zendy; Aseem Chandel | Zendy; Devendra Singh Chauhan | Zendy

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Neural Nework-Based Time Series Methods for Load Forecasting

Author(s) -

Girraj Singh,

Aseem Chandel,

Devendra Singh Chauhan

Publication year - 2020

Publication title -

international journal of recent technology and engineering (ijrte)

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 , artificial neural network , time series , nonlinear system , electric power system , power (physics) , star model , autoregressive integrated moving average , control theory (sociology) , mathematical optimization , control engineering , econometrics , artificial intelligence , engineering , 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.

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