Neural Nework-Based Time Series Methods for Load Forecasting
Girraj Singh1, Aseem Chandel2, D. S. Chauhan3
1Girraj Singh, BSACET, Mathura, UP.
2Aseem Chandel, REC, Mainpuri, UP.
3D. S. Chauhan, GLA University, Mathura, UP.
Manuscript received on April 30, 2020. | Revised Manuscript received on May 06, 2020. | Manuscript published on May 30, 2020. | PP: 2441-2444 | Volume-9 Issue-1, May 2020. | Retrieval Number: A2942059120/2020©BEIESP | DOI: 10.35940/ijrte.A2942.059120
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Short-Term Load Forecasting (STLF), Neural Network, Time Series, and MAPE, NARX.
Scope of the Article: Neural Network