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Comprehensive Evaluation for Operating Efficiency of Electricity Retail Companies Based on the Improved TOPSIS Method and LSSVM Optimized by Modified Ant Colony Algorithm from the View of Sustainable Development

Author

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  • Dongxiao Niu

    (School of Economics and Management, North China Electric Power University, Changping, Beijing 102206, China
    Beijing Key Laboratory of New Energy and Low-Carbon Development (North China Electric Power University), Changping, Beijing 102206, China)

  • Si Li

    (School of Economics and Management, North China Electric Power University, Changping, Beijing 102206, China
    Beijing Key Laboratory of New Energy and Low-Carbon Development (North China Electric Power University), Changping, Beijing 102206, China)

  • Shuyu Dai

    (School of Economics and Management, North China Electric Power University, Changping, Beijing 102206, China
    Beijing Key Laboratory of New Energy and Low-Carbon Development (North China Electric Power University), Changping, Beijing 102206, China)

Abstract

The electricity market of China is currently in the process of a new institutional reform. Diversified electricity retail entities are gradually being established with the opening of the marketing electricity side. In the face of a complex market environment and fierce competition, the operating efficiency can directly reflect the current market position and development of electricity retail companies. TOPSIS method can make full use of the information of original data, calculate the distance between evaluated objects and the ideal solutions and get the relative proximity, which is generally used in the overall department and comprehensive evaluation of the benefits. Least squares support vector machine (LSSVM), with high convergence precision, helps save the training time of algorithm by solving linear equations and is used to predict the comprehensive evaluation value. Considering the ultimate goal of sustainable development, a comprehensive evaluation model on operating efficiency of electricity retail companies based on the improved TOPSIS method and LSSVM optimized by modified ant colony algorithm is proposed in this paper. Firstly, from the view of sustainable development, an operating efficiency evaluation indicator system is constructed. Secondly, the entropy weight method is applied to empower the indicators objectively. After that, based on the improved TOPSIS method, the reverse problem in the evaluation process is eliminated. According to the relative proximity between the evaluated objects and the absolute ideal solutions, the scores of comprehensive evaluation for operating efficiency can then be ranked. Finally, the LSSVM optimized by modified ant colony algorithm is introduced to realize the simplified expert scoring process and fast calculation in the comprehensive evaluation process, and its improved learning and generalization ability can be used in the comprehensive evaluation of similar projects. The example analysis proves that the comprehensive evaluation model proposed in this paper can provide scientific and effective evaluation results of the operating efficiency of electricity retail companies.

Suggested Citation

  • Dongxiao Niu & Si Li & Shuyu Dai, 2018. "Comprehensive Evaluation for Operating Efficiency of Electricity Retail Companies Based on the Improved TOPSIS Method and LSSVM Optimized by Modified Ant Colony Algorithm from the View of Sustainable ," Sustainability, MDPI, vol. 10(3), pages 1-26, March.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:3:p:860-:d:136864
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. Enci Liu & Jie Li & Anni Zheng & Haoran Liu & Tao Jiang, 2022. "Research on the Prediction Model of the Used Car Price in View of the PSO-GRA-BP Neural Network," Sustainability, MDPI, vol. 14(15), pages 1-19, July.
    3. Ming Meng & Shucheng Wu & Jin Zhou & Xinfang Wang, 2019. "What is Currently Driving the Growth of China’s Household Electricity Consumption? A Clustering and Decomposition Analysis," Sustainability, MDPI, vol. 11(17), pages 1-14, August.
    4. Sen Guo & Wenyue Zhang & Xiao Gao, 2020. "Business Risk Evaluation of Electricity Retail Company in China Using a Hybrid MCDM Method," Sustainability, MDPI, vol. 12(5), pages 1-21, March.
    5. Jun Dong & Dongxue Wang & Dongran Liu & Palidan Ainiwaer & Linpeng Nie, 2019. "Operation Health Assessment of Power Market Based on Improved Matter-Element Extension Cloud Model," Sustainability, MDPI, vol. 11(19), pages 1-25, October.
    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.

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