A Hybrid Soft Computing Framework for Electrical Energy Optimization
Electricity is a significant and essential player in the modern world economy. It translates into the social, economic, and sectorial growth of any region. The scarcity of these resources demands a highly efficient and robust energy management system (EMS). In the recent literature, many artificial...
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| 主要な著者: | , , |
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| フォーマット: | Conference or Workshop Item |
| 言語: | English English |
| 出版事項: |
IEEE
2021
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| 主題: | |
| オンライン･アクセス: | http://umpir.ump.edu.my/id/eprint/38583/1/A%20Hybrid%20Soft%20Computing%20Framework%20for%20Electrical%20Energy%20Optimization%20partial.pdf http://umpir.ump.edu.my/id/eprint/38583/2/IEEE_A_Hybrid_Soft_Computing_Framework_for_Electrical_Energy_Optimization.pdf http://umpir.ump.edu.my/id/eprint/38583/ https://doi.org/10.1109/IMTIC53841.2021.9719856 |
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| 要約: | Electricity is a significant and essential player in the modern world economy. It translates into the social, economic, and sectorial growth of any region. The scarcity of these resources demands a highly efficient and robust energy management system (EMS). In the recent literature, many artificial intelligence algorithms have been proposed to cater to the need for efficient and real-time decision-making. Moreover, the hybridization of these algorithms has also been proposed for optimum decision-making. In this paper, a hybrid soft-computing-based framework has been proposed for intelligent energy management and optimization. The proposed model has based on the evolutionary neuro-fuzzy approach that can predict the energy demand as an objective function and optimize the energy within the given constraints. The future extension of this work will be the implementation and validation of the proposed framework on either a real application dataset or dataset opted from the benchmark repository |
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