In this study,a Neural Network-Enhanced Gene Modification Optimization Technique was introduced for multiobjective energy resource management.Addressing the need for sustainable energy solutions,this technique integra...In this study,a Neural Network-Enhanced Gene Modification Optimization Technique was introduced for multiobjective energy resource management.Addressing the need for sustainable energy solutions,this technique integrated neural network models as fitness functions,representing an advancement in artificial intelligencedriven optimization.Data collected in the European Union covered greenhouse gas emissions,energy consumption by sources,energy imports,and Levelized Cost of Energy.Since different configurations of energy consumption by sources lead to varying greenhouse gas emissions,costs,and imports,neural network prediction models were used to project the effect of new energy combinations on these variables.The projections were then fed into the gene modification optimization process to identify optimal configurations.Over 28 generations,simulations demonstrated a 46 percent reduction in energy costs and a 9 percent decrease in emissions.Human bias and subjectivity were mitigated by automating parameter settings,enhancing the objectivity of results.Benchmarking against traditional methods,such as Euclidean Distance,validated the superior performance of this approach.Furthermore,the technique’s ability to visualize chromosomes and gene values offered clarity in optimization processes.These results suggest significant advancements in the energy sector and potential applications in other industries,contributing to the global effort to combat climate change.展开更多
基金Open Access funding provided by Hungarian Electronic Information Services National Programme(EISZ)-Corvinus University of Budapest。
文摘In this study,a Neural Network-Enhanced Gene Modification Optimization Technique was introduced for multiobjective energy resource management.Addressing the need for sustainable energy solutions,this technique integrated neural network models as fitness functions,representing an advancement in artificial intelligencedriven optimization.Data collected in the European Union covered greenhouse gas emissions,energy consumption by sources,energy imports,and Levelized Cost of Energy.Since different configurations of energy consumption by sources lead to varying greenhouse gas emissions,costs,and imports,neural network prediction models were used to project the effect of new energy combinations on these variables.The projections were then fed into the gene modification optimization process to identify optimal configurations.Over 28 generations,simulations demonstrated a 46 percent reduction in energy costs and a 9 percent decrease in emissions.Human bias and subjectivity were mitigated by automating parameter settings,enhancing the objectivity of results.Benchmarking against traditional methods,such as Euclidean Distance,validated the superior performance of this approach.Furthermore,the technique’s ability to visualize chromosomes and gene values offered clarity in optimization processes.These results suggest significant advancements in the energy sector and potential applications in other industries,contributing to the global effort to combat climate change.
