Time Series Forecasting of Electrical Energy Consumption Using Deep Learning Algorithm

Authors

  • E. O. Edoka Department of Electrical and Electronic Engineering, Edo State University Uzairue,
  • V. K. Abanihi Department of Electrical and Electronic Engineering, Edo State University Uzairue
  • H. E. Amhenrior Department of Electrical and Electronic Engineering, Edo State University Uzairue, Uzairue
  • E. M. J. Evbogbai Department of Electrical and Electronic Engineering, Edo State University Uzairue
  • L. O. Bello Department of Electrical and Electronic Engineering, Edo State University Uzairue
  • V. Oisamoje Department of Electrical and Electronic Engineering, Edo State University Uzairue

Keywords:

Energy consumption, Load forecasting, Short-term forecast, Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNN)

Abstract

Energy consumption forecasting is an operation of predicting the future energy consumption of electrical systems using previous or historical data. The Long Short-term Memory (LSTM) Model; a deep learning model was used in this project to analyze the Short-term consumption forecast performance. This was carried out by using an energy consumption dataset obtained from the Transmission Company of Nigeria (TCN) Benin City regional 132/33KV transmission station. The dataset were daily load readings recorded in the half-hourly format from August to December 2021. The model was used to demonstrate the feasibility of generating an accurate short-term load forecast for the case study despite the peculiarity and insufficiency of the energy consumption readings. Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) are the statistical evaluation metrics used. The approach produces exceptional levels of accuracy, with MAPE of 0.010 and RMSE of 19.759 for a 100 time-step. The findings imply that the LSTM model can make accurate predictions with minimal error, and this Deep learning model may be a useful tool for short-term forecasting demand. This finding serves as a baseline for future research in this field of study and power system planning.

 

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Published

2023-09-16

Issue

Vol. 20 No. 3 (2023): Volume 20, Issue 3, September 2023

Section

Articles

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