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A new forecasting framework for volatile behavior in net electricity consumption: A case study in Turkey

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  • Tutun, Salih
  • Chou, Chun-An
  • Canıyılmaz, Erdal

Abstract

Electricity is a significant form of energy that cannot be stored physically and is usually generated as needed. In most research studies, the main aim is to ensure that sufficient electricity is generated to meet future needs. In order to avoid waste or shortage, a good system needs to be designed to constantly maintain the level of electricity needed. It is necessary to estimate independent factors because future electricity volume is based not only on current net consumption but also on independent factors. In this paper, a new framework is proposed to first estimate future independent factors using SARIMA (seasonal auto-regressive iterative moving average) method and NARANN (nonlinear autoregressive artificial neural network) method, both of which are called a ”forecasted scenario approach”. Subsequently, based on these scenarios, a LADES (LASSO-based adaptive evolutionary simulated annealing) model and a RADES (ridge-based adaptive evolutionary simulated annealing) model are applied to forecast the future NEC (net electricity consumption). The proposed approaches are then validated with a case study in Turkey. The experimental results show that our approach outperforms others when compared to previous approaches. Finally, the results show that the NEC can be modeled, and it can be used to predict the future NEC.

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  • Tutun, Salih & Chou, Chun-An & Canıyılmaz, Erdal, 2015. "A new forecasting framework for volatile behavior in net electricity consumption: A case study in Turkey," Energy, Elsevier, vol. 93(P2), pages 2406-2422.
    • Handle: RePEc:eee:energy:v:93:y:2015:i:p2:p:2406-2422
    DOI: 10.1016/j.energy.2015.10.064
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    References listed on IDEAS

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    Cited by:

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    2. Yi-Chung Hu, 2017. "Electricity consumption prediction using a neural-network-based grey forecasting approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(10), pages 1259-1264, October.
    3. Bilgili, Mehmet & Pinar, Engin, 2023. "Gross electricity consumption forecasting using LSTM and SARIMA approaches: A case study of Türkiye," Energy, Elsevier, vol. 284(C).
    4. He, Yaoyao & Zheng, Yaya, 2018. "Short-term power load probability density forecasting based on Yeo-Johnson transformation quantile regression and Gaussian kernel function," Energy, Elsevier, vol. 154(C), pages 143-156.
    5. Sehrish Malik & DoHyeun Kim, 2018. "Prediction-Learning Algorithm for Efficient Energy Consumption in Smart Buildings Based on Particle Regeneration and Velocity Boost in Particle Swarm Optimization Neural Networks," Energies, MDPI, vol. 11(5), pages 1-21, May.
    6. Carolina Deina & João Lucas Ferreira dos Santos & Lucas Henrique Biuk & Mauro Lizot & Attilio Converti & Hugo Valadares Siqueira & Flavio Trojan, 2023. "Forecasting Electricity Demand by Neural Networks and Definition of Inputs by Multi-Criteria Analysis," Energies, MDPI, vol. 16(4), pages 1-24, February.
    7. Weijia Shao & Lukas Friedemann Radke & Fikret Sivrikaya & Sahin Albayrak, 2021. "Adaptive Online Learning for the Autoregressive Integrated Moving Average Models," Mathematics, MDPI, vol. 9(13), pages 1-30, June.
    8. Qasem Abu Al-Haija, 2021. "A Stochastic Estimation Framework for Yearly Evolution of Worldwide Electricity Consumption," Forecasting, MDPI, vol. 3(2), pages 1-11, April.
    9. Akdi, Yılmaz & Gölveren, Elif & Okkaoğlu, Yasin, 2020. "Daily electrical energy consumption: Periodicity, harmonic regression method and forecasting," Energy, Elsevier, vol. 191(C).
    10. Yi-Chung Hu, 2017. "Nonadditive Grey Prediction Using Functional-Link Net for Energy Demand Forecasting," Sustainability, MDPI, vol. 9(7), pages 1-14, July.
    11. Yi-Chung Hu & Peng Jiang, 2017. "Forecasting energy demand using neural-network-based grey residual modification models," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(5), pages 556-565, May.
    12. Kaytez, Fazil, 2020. "A hybrid approach based on autoregressive integrated moving average and least-square support vector machine for long-term forecasting of net electricity consumption," Energy, Elsevier, vol. 197(C).
    13. Kaboli, S. Hr. Aghay & Selvaraj, J. & Rahim, N.A., 2016. "Long-term electric energy consumption forecasting via artificial cooperative search algorithm," Energy, Elsevier, vol. 115(P1), pages 857-871.
    14. Li, Xuetao & Wang, Ziwei & Yang, Chengying & Bozkurt, Ayhan, 2024. "An advanced framework for net electricity consumption prediction: Incorporating novel machine learning models and optimization algorithms," Energy, Elsevier, vol. 296(C).

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