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Prediction and empirical research on China’s electricity consumption and energy consumption per unit GDP based on AI-driven quantum group intelligence algorithms—a hybrid model of KRLS and QFOA-SVR
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DOI：10.3389/fenrg.2026.1822035.png)
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Abstract
En 中文
As a cornerstone of the national economy; the energy sector plays a critical role in ensuring energy security and advancing the Dual Carbon goals. However; existing forecasting models often fail to capture systematic information in regression residuals and neglect the stochastic nature of optimization algorithms; raising concerns about reproducibility. To fill these gaps; this study develops a novel hybrid modeling framework that integrates Kernel Ridge Regression (KRLS) with Support Vector Regression (SVR) optimized by the Fruit Fly Optimization Algorithm (FOA) and its quantum-enhanced variant (QFOA). A two-stage “baseline fitting plus residual correction” paradigm is established to extract implicit information from residuals. All experiments are implemented under a rigorous reproducibility protocol with a fixed random seed and 30 repeated runs. Based on 300 provincial observations from 2014 to 2023; the empirical results show that QFOA-SVR + KRLS delivers the best performance for electricity consumption (Y1); while FOA-SVR + KRLS achieves a higher R2 for energy intensity (Y2). The models also capture significant provincial heterogeneity; with eastern regions showing lower prediction errors than western and northern areas. This study provides a reproducible and regionally adaptive foundation for formulating differentiated energy policies in China.
Keywords:
energy consumption
SVR
energy economics
forecasting model
KRLS model
Journal
IF:
2.4
Papers:
923
Citations:
1.4W
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Cited Papers