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Research Article |

Electrical Load Forecasting using ARIMA, Prophet and LSTM Networks

Author(s): Durga Prasad Ananthu* and Prof. Neelashetty K

Published In : International Journal of Electrical and Electronics Research (IJEER)        volume 9, issue 4

Publisher : FOREX Publication

Published : 30 December 2021

e-ISSN : 2347-470X

Page(s) : 114-119

DOI: https://doi.org/10.37391/IJEER.090404

Abstract

Forecasting electrical load plays a vital role in power system planning. However, it is quite difficult to forecast electrical load, as the load on the system varies continuously concerning time and seasons. In this paper, we are proposing an advanced artificial neural network model to forecast short-term electrical load. The proposed method tested on historical data collected from Karnataka power corporation, India, and test results compared with other data-driven models viz. ARIMA, RNN, LSTM, and Prophet. The accuracy and RMSE values were calculated and observed that the proposed model was superior in a day and weekly ahead electrical load forecasting.

Keywords: Load forecasting, ARIMA, RNN, LSTM (Long Short-Term Memory), Artificial Neural Network (ANN), Power System Planning.




Durga Prasad Ananthu*, Research Scholar, VTU-RRC, Belagavi, India; Email: adp.ananthu@gmail.com

Prof. Neelashetty K, Professor, DoEE, GND, Bidar, India

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Durga Prasad Ananthu and Prof. Neelashetty K (2021), Electrical Load Forecasting using ARIMA, Prophet and LSTM Networks. IJEER 9(4), 114-119. DOI: 10.37391/IJEER.090404.