JCEEES
Journal of Computer & Electrical and Electronics Engineering Sciences

JCEEES aims to publish original articles covering the theoretical foundations of major computer, electronic and electrical engineering sciences, as well as academic, commercial and educational aspects that propose new ideas for the application and design of artificial intelligence, software and information systems. In addition to wide-ranging regular topics, JCEEES also makes it a principle to include special topics covering specific topics in all areas of interest mainly in computational medicine, artificial intelligence, computer science, and electrical & electronic engineering science.

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Original Article
Hourly energy consumption forecasting by LSTM and ARIMA methods
Abstract
Energy consumption represents the overall quantity of energy necessary for a specific activity or process, typically quantified in kilowatt-hours (kWh). In 2019, global electricity final consumption amounted to 22,848 terawatt-hours (TWh), indicating a 1.7% growth compared to 2018. During the same period, the Organization for Economic Co-operation and Development (OECD) recorded a total electricity final consumption of 9,672 TWh, reflecting a 1.1% decrease from the previous year. Conversely, non-OECD countries experienced a rise in final electricity consumption, reaching 13,176 TWh, marking a 3.8% increase over the 2018 figures. Time series analysis is a statistical method that examines data collected over successive time intervals. By identifying patterns and trends in historical data, this approach facilitates predictions and forecasts about future values. In our problem, forecasting and estimating the energy consumption in megawatts for the west regions of USA have achieved a good performance, with 97% for the R-squared metrics for LSTM and 98% for ARIMA models.

Keywords : Energy consumption, time series analysis, long short-term memory (LSTM), auto-regressive integrated moving average( ARIMA)

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Article available in :
Volume 3, Issue 1, 2025
Page : 14-20
https://doi.org/10.51271/JCEEES-0023
Article Information
Received : 23 Eki 2024
Accepted : 08 Oca 2025
Published : 17 Nis 2025
Corresponding Author :

Taha Etem: Department of Computer Engineering, Faculty of Engineering, Çankırı Karatekin University, Çankırı, Turkiye

[email protected]
Citation : Ali, A.N., & Etem, T. (2025). Hourly energy consumption forecasting by LSTM and ARIMA methods. J Comp Electr Electron Eng Sci, 3(1),14-20.

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