Comparative Analysis of Electricity Consumption Forecast

Mehmet Ali Arslan; Tarık Talan

doi:10.38088/jise.1619782

EN

Comparative Analysis of Electricity Consumption Forecast

Abstract

This study aims to make a comparative analysis of electricity consumption forecast using artificial intelligence (AI) and statistical models. In order to reduce the current deficits of countries, it is of great importance to predict the future electricity consumption amount and plan the power plant capacities accordingly. Electricity is an energy source that is extremely difficult to store when used in sectors such as industry and housing. Therefore, the electricity produced must be consumed immediately without causing energy losses and waste. In this context, ensuring the balance between electricity production and consumption can correctly contribute to the management of the current deficit by increasing economic efficiency. In the current study, Türkiye's hourly electricity consumption data between 2016 and 2024 were examined. These data were transformed into a 108-month consumption data set. Seven different models, namely Auto-ARIMA, Holt-Winters, Theta, ETS, TBATS, NNETAR and MLP, were used in the analyses. Among the models, NNETAR and MLP are AI based, and the others are statistical-based models. In this way, the effectiveness of different model types in electricity consumption estimations was compared. In this study, the Auto-ARIMA model stood out with a 3.77% MAPE error rate. When such studies are considered within the framework of countries' energy policies, they can make a significant contribution to reducing the current deficit of the country's economy. As a result of the study, it was concluded that the Auto-ARIMA model should be taken into consideration when making estimates on how many Megawatt power plants should be built in order to meet future energy needs in shaping energy policies in Türkiye.

Keywords

References

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Details

Primary Language

English

Subjects

Artificial Intelligence (Other)

Journal Section

Research Article

Authors

Mehmet Ali Arslan ^*
0000-0002-0701-059X
Türkiye

Tarık Talan
0000-0002-5371-4520
Türkiye

Early Pub Date

June 11, 2025

Publication Date

June 17, 2025

Submission Date

January 14, 2025

Acceptance Date

April 17, 2025

Published in Issue

Year 2025 Volume: 9 Number: 1

DOI

https://doi.org/10.38088/jise.1619782

IZ

https://izlik.org/JA23RC25WC

Cite

RIS / Bibtex

APA

Arslan, M. A., & Talan, T. (2025). Comparative Analysis of Electricity Consumption Forecast. Journal of Innovative Science and Engineering, 9(1), 89-102. https://doi.org/10.38088/jise.1619782

AMA

1.Arslan MA, Talan T. Comparative Analysis of Electricity Consumption Forecast. JISE. 2025;9(1):89-102. doi:10.38088/jise.1619782

Chicago

Arslan, Mehmet Ali, and Tarık Talan. 2025. “Comparative Analysis of Electricity Consumption Forecast”. Journal of Innovative Science and Engineering 9 (1): 89-102. https://doi.org/10.38088/jise.1619782.

EndNote

Arslan MA, Talan T (June 1, 2025) Comparative Analysis of Electricity Consumption Forecast. Journal of Innovative Science and Engineering 9 1 89–102.

IEEE

[1]M. A. Arslan and T. Talan, “Comparative Analysis of Electricity Consumption Forecast”, JISE, vol. 9, no. 1, pp. 89–102, June 2025, doi: 10.38088/jise.1619782.

ISNAD

Arslan, Mehmet Ali - Talan, Tarık. “Comparative Analysis of Electricity Consumption Forecast”. Journal of Innovative Science and Engineering 9/1 (June 1, 2025): 89-102. https://doi.org/10.38088/jise.1619782.

JAMA

1.Arslan MA, Talan T. Comparative Analysis of Electricity Consumption Forecast. JISE. 2025;9:89–102.

MLA

Arslan, Mehmet Ali, and Tarık Talan. “Comparative Analysis of Electricity Consumption Forecast”. Journal of Innovative Science and Engineering, vol. 9, no. 1, June 2025, pp. 89-102, doi:10.38088/jise.1619782.

Vancouver

1.Mehmet Ali Arslan, Tarık Talan. Comparative Analysis of Electricity Consumption Forecast. JISE. 2025 Jun. 1;9(1):89-102. doi:10.38088/jise.1619782

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Öz

Comparative Analysis of Electricity Consumption Forecast

Abstract

Keywords

References

Details

Primary Language

Subjects

Journal Section

Authors

Early Pub Date

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

Cite

Cited By

Simulation and Full-Factorial Design Response Optimizer for Solar Water Heating Energy System