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<p>This paper proposes a hybrid method using renewable energy resources for Energy Hub system energy management. The proposed hybrid approach is the combined execution of Gorilla Troops optimization Algorithm (GTOA) and Attention based Convolutional Neural Network (ACNN). Therefore, it is known as GTOA-ACNN approach. The energy hub is a new idea for multicarrier energy systems; it can transmit, receive, and store several types of energy. The development of multi-carrier energy hub systems and the expansion of distributed generation demonstrate the necessity for energy hub systems. The system's primary goal is to lower the overall operating costs of the energy storage devices, wind energy, boiler, and Combined Heating and Power (CHP) unit. The goal of this research is to reduce the system's annual operating and capital expenses. The stochastic planning and scheduling based on the GTOA approach is used to optimize the parameters of Gas network data, Weather data, Economic data, Electrical network data, Loads, DG data.The Attention-based Convolutional Neural Network (ACNN) predict the stochastic parameters. The parameters like cooling and heating loads. The MATLAB working platform is used to put into practice the proposed model. The proposed method displays total operational cost is 1.83 <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="10668_2025_6239_Article_IEq1.gif" Format="GIF" Height="13" Rendition="HTML" Resolution="72" Type="Linedraw" Width="19" /> </InlineMediaObject> <EquationSource Format="TEX">\(\times\)</EquationSource> <EquationSource Format="MATHML"><math> <mo>×</mo> </math></EquationSource> </InlineEquation> 10<sup>7</sup>$ which is low, emission is 7640.974&#xa0;kg which is low and the computation time of the proposed technique is 500&#xa0;s it is low compared with other existing techniques.</p>

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Energy management of energy hub with renewable energy resources based on GTOA-ACNN approach

  • 期刊论文

  • 发表日期：

    2025年5月7日

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  • R. Krishnakumar,
  • U. Arun Kumar,
  • V. Gomathy,
  • N. A. Natraj

摘要

This paper proposes a hybrid method using renewable energy resources for Energy Hub system energy management. The proposed hybrid approach is the combined execution of Gorilla Troops optimization Algorithm (GTOA) and Attention based Convolutional Neural Network (ACNN). Therefore, it is known as GTOA-ACNN approach. The energy hub is a new idea for multicarrier energy systems; it can transmit, receive, and store several types of energy. The development of multi-carrier energy hub systems and the expansion of distributed generation demonstrate the necessity for energy hub systems. The system's primary goal is to lower the overall operating costs of the energy storage devices, wind energy, boiler, and Combined Heating and Power (CHP) unit. The goal of this research is to reduce the system's annual operating and capital expenses. The stochastic planning and scheduling based on the GTOA approach is used to optimize the parameters of Gas network data, Weather data, Economic data, Electrical network data, Loads, DG data.The Attention-based Convolutional Neural Network (ACNN) predict the stochastic parameters. The parameters like cooling and heating loads. The MATLAB working platform is used to put into practice the proposed model. The proposed method displays total operational cost is 1.83 \(\times\) × 107$ which is low, emission is 7640.974 kg which is low and the computation time of the proposed technique is 500 s it is low compared with other existing techniques.