Machine learning methods to assist energy system optimization
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摘要
This study evaluates the potential of supervised and transfer learning techniques to assist energy system optimization. A surrogate model is developed with the support of a supervised learning technique( by using artificial neural network) in order to bypass computationally intensive Actual Engineering Model( AEM). Eight different neural network architectures are considered in the process of developing the surrogate model. Subsequently,a hybrid optimization algorithm( HOA) is developed combining Surrogate and AEM in order to speed up the optimization process while maintaining the accuracy. Pareto optimization is conducted considering Net Present Value and Grid Integration level as the objective functions. Transfer learning is used to adapt the surrogate model( trained using supervised learning technique) for different scenarios where solar energy potential,wind speed and energy demand are notably different. Results reveal that the surrogate model can reach to Pareto solutions with a higher accuracy when grid interactions are above 10%( with reasonable differences in the decision space variables). HOA can reach to Pareto solutions( similar to the solutions obtained using AEM) around 17 times faster than AEM. The Surrogate Models developed using Transfer Learning( SMTL) shows a similar capability. SMTL combined with the optimization algorithm can predict Pareto fronts efficiently even when there are significant changes in the initial conditions.Therefore,STML can be used along with the HOA,which reduces the computational time required for energy system optimization by 84%. Such a significant reduction in computational time enables the approach to be used for energy system optimization at regional or national scale.
This study evaluates the potential of supervised and transfer learning techniques to assist energy system optimization. A surrogate model is developed with the support of a supervised learning technique( by using artificial neural network) in order to bypass computationally intensive Actual Engineering Model( AEM). Eight different neural network architectures are considered in the process of developing the surrogate model. Subsequently,a hybrid optimization algorithm( HOA) is developed combining Surrogate and AEM in order to speed up the optimization process while maintaining the accuracy. Pareto optimization is conducted considering Net Present Value and Grid Integration level as the objective functions. Transfer learning is used to adapt the surrogate model( trained using supervised learning technique) for different scenarios where solar energy potential,wind speed and energy demand are notably different. Results reveal that the surrogate model can reach to Pareto solutions with a higher accuracy when grid interactions are above 10%( with reasonable differences in the decision space variables). HOA can reach to Pareto solutions( similar to the solutions obtained using AEM) around 17 times faster than AEM. The Surrogate Models developed using Transfer Learning( SMTL) shows a similar capability. SMTL combined with the optimization algorithm can predict Pareto fronts efficiently even when there are significant changes in the initial conditions.Therefore,STML can be used along with the HOA,which reduces the computational time required for energy system optimization by 84%. Such a significant reduction in computational time enables the approach to be used for energy system optimization at regional or national scale.
This study evaluates the potential of supervised and transfer learning techniques to assist energy system optimization. A surrogate model is developed with the support of a supervised learning technique( by using artificial neural network) in order to bypass computationally intensive Actual Engineering Model( AEM). Eight different neural network architectures are considered in the process of developing the surrogate model. Subsequently,a hybrid optimization algorithm( HOA) is developed combining Surrogate and AEM in order to speed up the optimization process while maintaining the accuracy. Pareto optimization is conducted considering Net Present Value and Grid Integration level as the objective functions. Transfer learning is used to adapt the surrogate model( trained using supervised learning technique) for different scenarios where solar energy potential,wind speed and energy demand are notably different. Results reveal that the surrogate model can reach to Pareto solutions with a higher accuracy when grid interactions are above 10%( with reasonable differences in the decision space variables). HOA can reach to Pareto solutions( similar to the solutions obtained using AEM) around 17 times faster than AEM. The Surrogate Models developed using Transfer Learning( SMTL) shows a similar capability. SMTL combined with the optimization algorithm can predict Pareto fronts efficiently even when there are significant changes in the initial conditions.Therefore,STML can be used along with the HOA,which reduces the computational time required for energy system optimization by 84%. Such a significant reduction in computational time enables the approach to be used for energy system optimization at regional or national scale.
引文