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- W2767259287 abstract "Accurate electricity forecasting is still the critical issue in many energy management fields. The applications of hybrid novel algorithms with support vector regression (SVR) models to overcome the premature convergence problem and improve forecasting accuracy levels also deserve to be widely explored. This paper applies chaotic function and quantum computing concepts to address the embedded drawbacks including crossover and mutation operations of genetic algorithms. Then, this paper proposes a novel electricity load forecasting model by hybridizing chaotic function and quantum computing with GA in an SVR model (named SVRCQGA) to achieve more satisfactory forecasting accuracy levels. Experimental examples demonstrate that the proposed SVRCQGA model is superior to other competitive models." @default.
- W2767259287 created "2017-11-17" @default.
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- W2767259287 date "2017-11-10" @default.
- W2767259287 modified "2023-10-16" @default.
- W2767259287 title "Applications of the Chaotic Quantum Genetic Algorithm with Support Vector Regression in Load Forecasting" @default.
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- W2767259287 doi "https://doi.org/10.3390/en10111832" @default.
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