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Sustainability Evaluation of Power Grid Construction Projects Using Improved TOPSIS and Least Square Support Vector Machine with Modified Fly Optimization Algorithm

Author

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  • Dongxiao Niu

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Yan Li

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Shuyu Dai

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Hui Kang

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Zhenyu Xue

    (State Grid Economic and Technological Research Institute Co., Ltd., Beijing 102210, China)

  • Xianing Jin

    (State Grid Economic and Technological Research Institute Co., Ltd., Beijing 102210, China)

  • Yi Song

    (State Grid Economic and Technological Research Institute Co., Ltd., Beijing 102210, China)

Abstract

The electric power industry is of great significance in promoting social and economic development and improving people’s living standards. Power grid construction is a necessary part of infrastructure construction, whose sustainability plays an important role in economic development, environmental protection and social progress. In order to effectively evaluate the sustainability of power grid construction projects, in this paper, we first identified 17 criteria from four dimensions including economy, technology, society and environment to establish the evaluation criteria system. After that, the grey incidence analysis was used to modify the traditional Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), which made it possible to evaluate the sustainability of electric power construction projects based on visual angle of similarity and nearness. Then, in order to simplify the procedure of experts scoring and computation, on the basis of evaluation results of the improved TOPSIS, the model using Modified Fly Optimization Algorithm (MFOA) to optimize the Least Square Support Vector Machine (LSSVM) was established. Finally, a numerical example was given to demonstrate the effectiveness of the proposed model.

Suggested Citation

  • Dongxiao Niu & Yan Li & Shuyu Dai & Hui Kang & Zhenyu Xue & Xianing Jin & Yi Song, 2018. "Sustainability Evaluation of Power Grid Construction Projects Using Improved TOPSIS and Least Square Support Vector Machine with Modified Fly Optimization Algorithm," Sustainability, MDPI, vol. 10(1), pages 1-19, January.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:1:p:231-:d:127452
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    References listed on IDEAS

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    Cited by:

    1. Mateusz Piwowarski & Danuta Miłaszewicz & Małgorzata Łatuszyńska & Mariusz Borawski & Kesra Nermend, 2018. "Application of the Vector Measure Construction Method and Technique for Order Preference by Similarity Ideal Solution for the Analysis of the Dynamics of Changes in the Poverty Levels in the European ," Sustainability, MDPI, vol. 10(8), pages 1-24, August.
    2. Xing Zhang, 2018. "Short-Term Load Forecasting for Electric Bus Charging Stations Based on Fuzzy Clustering and Least Squares Support Vector Machine Optimized by Wolf Pack Algorithm," Energies, MDPI, vol. 11(6), pages 1-18, June.
    3. Chiou-Jye Huang & Ping-Huan Kuo, 2018. "A Short-Term Wind Speed Forecasting Model by Using Artificial Neural Networks with Stochastic Optimization for Renewable Energy Systems," Energies, MDPI, vol. 11(10), pages 1-20, October.
    4. Guangqi Liang & Dongxiao Niu & Yi Liang, 2020. "Core Competitiveness Evaluation of Clean Energy Incubators Based on Matter-Element Extension Combined with TOPSIS and KPCA-NSGA-II-LSSVM," Sustainability, MDPI, vol. 12(22), pages 1-26, November.
    5. Ehsan Javanmardi & Sifeng Liu & Naiming Xie, 2020. "Exploring Grey Systems Theory-Based Methods and Applications in Sustainability Studies: A Systematic Review Approach," Sustainability, MDPI, vol. 12(11), pages 1-32, May.
    6. Zhen Li & Yun Li & Yanbin Li, 2019. "Performance Evaluation of Energy Transition Based on the Technique for Order Preference by a Similar to Ideal Solution and Support Vector Machine Optimized by an Improved Artificial Bee Colony Algorit," Energies, MDPI, vol. 12(16), pages 1-21, August.
    7. Ping-Huan Kuo & Chiou-Jye Huang, 2018. "An Electricity Price Forecasting Model by Hybrid Structured Deep Neural Networks," Sustainability, MDPI, vol. 10(4), pages 1-17, April.

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