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Journals / Alphanumeric Journal / 2026 / Volume: 12 Issue: 3 Special Issue

China Total Energy Consumption Forecast with Optimized Continuous Conformable Fractional Grey Model

Halis Bilgil(KAYSERI UNIVERSITY)0000-0002-8329-5806
Ümmügülsüm Erdinç(AKSARAY UNIVERSITY)0000-0002-4504-3675
Pages
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Publication Date
2024-12-31
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The obtained results indicate that the OCCFGM(1,1) model exhibits superior forecasting performance compared to the other models under consideration.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:One of the methods used for forecasting of the time series is the fractional grey modeling approach.
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Key Findings & Data:In this paper, the OCCFGM(1,1) model is utilized to forecasting of the total energy consumption data of China.
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Conclusion & Impact:The obtained results indicate that the OCCFGM(1,1) model exhibits superior forecasting performance compared to the other models under consideration.
🔬Design:Modeling & Simulation
📍Scope:2013 Verileri
⚙️Variables:Conformable fractional calculus · Energy consumption · Fractional grey model

Abstract

One of the methods used for forecasting of the time series is the fractional grey modeling approach. In this paper, the OCCFGM(1,1) model is utilized to forecasting of the total energy consumption data of China. The optimal values of $\alpha$ and $r$, which are fractional parameters in the model, are calculated using the Brute Force algorithm. Data collected from official sources from 2013 to 2022 are used to build the forecasting model, while data from 2013 to 2020 are employed to evaluate the accuracy at the model. The obtained results indicate that the OCCFGM(1,1) model exhibits superior forecasting performance compared to the other models under consideration.

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Cite This Article

Bilgil, H., Erdinç, Ü. (2024). China Total Energy Consumption Forecast with Optimized Continuous Conformable Fractional Grey Model. Alphanumeric Journal, Volume: 12 Issue: 3 Special Issue. https://doi.org/10.17093/alphanumeric.1447211

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