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Forecasting annual gross electricity demand by artificial neural networks using predicted values of socio-economic indicators and climatic conditions: Case of Turkey

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  • Günay, M. Erdem

Abstract

In this work, the annual gross electricity demand of Turkey was modeled by multiple linear regression and artificial neural networks as a function population, gross domestic product per capita, inflation percentage, unemployment percentage, average summer temperature and average winter temperature. Among these, the unemployment percentage and the average winter temperature were found to be insignificant to determine the demand for the years between 1975 and 2013. Next, the future values of the statistically significant variables were predicted by time series ANN models, and these were simulated in a multilayer perceptron ANN model to forecast the future annual electricity demand. The results were validated with a very high accuracy for the years that the electricity demand was known (2007–2013), and they were also superior to the official predictions (done by Ministry of Energy and Natural Resources of Turkey). The model was then used to forecast the annual gross electricity demand for the future years, and it was found that, the demand will be doubled reaching about 460TWh in the year 2028. Finally, it was concluded that the approach applied in this work can easily be implemented for other countries to make accurate predictions for the future.

Suggested Citation

  • Günay, M. Erdem, 2016. "Forecasting annual gross electricity demand by artificial neural networks using predicted values of socio-economic indicators and climatic conditions: Case of Turkey," Energy Policy, Elsevier, vol. 90(C), pages 92-101.
    • Handle: RePEc:eee:enepol:v:90:y:2016:i:c:p:92-101
    DOI: 10.1016/j.enpol.2015.12.019
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    Cited by:

    1. Mustafa Saglam & Catalina Spataru & Omer Ali Karaman, 2022. "Electricity Demand Forecasting with Use of Artificial Intelligence: The Case of Gokceada Island," Energies, MDPI, vol. 15(16), pages 1-22, August.
    2. Jiang, Ping & Li, Ranran & Liu, Ningning & Gao, Yuyang, 2020. "A novel composite electricity demand forecasting framework by data processing and optimized support vector machine," Applied Energy, Elsevier, vol. 260(C).
    3. Yuan, Xiao-Chen & Sun, Xun & Zhao, Weigang & Mi, Zhifu & Wang, Bing & Wei, Yi-Ming, 2017. "Forecasting China’s regional energy demand by 2030: A Bayesian approach," Resources, Conservation & Recycling, Elsevier, vol. 127(C), pages 85-95.
    4. Mustafa Saglam & Catalina Spataru & Omer Ali Karaman, 2023. "Forecasting Electricity Demand in Turkey Using Optimization and Machine Learning Algorithms," Energies, MDPI, vol. 16(11), pages 1-23, June.
    5. Li, Jinghua & Luo, Yichen & Wei, Shanyang, 2022. "Long-term electricity consumption forecasting method based on system dynamics under the carbon-neutral target," Energy, Elsevier, vol. 244(PA).

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