IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i17p10991-d905341.html
   My bibliography  Save this article

Smart Grid Project Benefit Evaluation Based on a Hybrid Intelligent Model

Author

Listed:
  • Yi Liang

    (School of Management, Hebei GEO University, Shijiazhuang 050031, China
    Strategy and Management Base of Mineral Resources in Hebei Province, Hebei GEO University, Shijiazhuang 050031, China)

  • Yingying Fan

    (School of Art, Hebei GEO University, Shijiazhuang 050031, China)

  • Yongfang Peng

    (School of Management, Hebei GEO University, Shijiazhuang 050031, China)

  • Haigang An

    (School of Management, Hebei GEO University, Shijiazhuang 050031, China)

Abstract

With the accelerated development of smart cities, the construction and development of smart grids have an increasing impact on the safe and stable operation of power systems. The benefit evaluation of smart grids can find out the problems of smart grids more comprehensively, which is of great practical significance for the further development of smart cities. In order to ensure accuracy and real-time evaluation, this paper proposes a new hybrid intelligent evaluation model using an improved technique for order preference by similarity to an ideal solution (TOPSIS) and long–short-term memory (LSTM) optimized by a modified sparrow search algorithm (MSSA). First, a set of smart grid benefit evaluation index systems is established in the context of considering smart city development. Then, aiming at the reverse order problem existing in TOPSIS, an improved evaluation model with entropy weight and modified TOPSIS is established. Finally, an intelligent evaluation model based on LSTM with MSSA optimization is designed. The example analysis verifies the accuracy of the model proposed, points out the important factors affecting the benefits of smart grids, and provides a new idea to achieve effective evaluation and rapid prediction, which can help to improve the benefit level of smart grids.

Suggested Citation

  • Yi Liang & Yingying Fan & Yongfang Peng & Haigang An, 2022. "Smart Grid Project Benefit Evaluation Based on a Hybrid Intelligent Model," Sustainability, MDPI, vol. 14(17), pages 1-20, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:17:p:10991-:d:905341
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/17/10991/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/17/10991/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Guoqing An & Ziyao Jiang & Libo Chen & Xin Cao & Zheng Li & Yuyang Zhao & Hexu Sun, 2021. "Ultra Short-Term Wind Power Forecasting Based on Sparrow Search Algorithm Optimization Deep Extreme Learning Machine," Sustainability, MDPI, vol. 13(18), pages 1-18, September.
    2. Spiros Livieratos & Vasiliki-Emmanouela Vogiatzaki & Panayotis G. Cottis, 2013. "A Generic Framework for the Evaluation of the Benefits Expected from the Smart Grid," Energies, MDPI, vol. 6(2), pages 1-21, February.
    3. Chengtian Ouyang & Donglin Zhu & Yaxian Qiu, 2021. "Lens Learning Sparrow Search Algorithm," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-17, May.
    4. Bemani, Amin & Xiong, Qingang & Baghban, Alireza & Habibzadeh, Sajjad & Mohammadi, Amir H. & Doranehgard, Mohammad Hossein, 2020. "Modeling of cetane number of biodiesel from fatty acid methyl ester (FAME) information using GA-, PSO-, and HGAPSO- LSSVM models," Renewable Energy, Elsevier, vol. 150(C), pages 924-934.
    5. Haichao Wang & Yi Liang & Wei Ding & Dongxiao Niu & Si Li & Fenghua Wang, 2020. "The Improved Least Square Support Vector Machine Based on Wolf Pack Algorithm and Data Inconsistency Rate for Cost Prediction of Substation Projects," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-14, December.
    6. Yi Liang & Haichao Wang, 2021. "Using Improved SPA and ICS-LSSVM for Sustainability Assessment of PV Industry along the Belt and Road," Energies, MDPI, vol. 14(12), pages 1-19, June.
    7. Liang, Yi & Niu, Dongxiao & Hong, Wei-Chiang, 2019. "Short term load forecasting based on feature extraction and improved general regression neural network model," Energy, Elsevier, vol. 166(C), pages 653-663.
    8. Hafeez, Ghulam & Khan, Imran & Jan, Sadaqat & Shah, Ibrar Ali & Khan, Farrukh Aslam & Derhab, Abdelouahid, 2021. "A novel hybrid load forecasting framework with intelligent feature engineering and optimization algorithm in smart grid," Applied Energy, Elsevier, vol. 299(C).
    9. Wu, Yunna & Zhang, Ting, 2021. "Risk assessment of offshore wave-wind-solar-compressed air energy storage power plant through fuzzy comprehensive evaluation model," Energy, Elsevier, vol. 223(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yanhua Chang & Yi Liang, 2023. "Intelligent Risk Assessment of Ecological Agriculture Projects from a Vision of Low Carbon," Sustainability, MDPI, vol. 15(7), pages 1-21, March.
    2. Wei Ding & Xuguang Zhao & Weigao Meng & Haichao Wang, 2022. "Smart Evaluation of Sustainability of Photovoltaic Projects in the Context of Carbon Neutrality Target," Sustainability, MDPI, vol. 14(22), pages 1-18, November.
    3. Dai, Yeming & Yang, Xinyu & Leng, Mingming, 2022. "Forecasting power load: A hybrid forecasting method with intelligent data processing and optimized artificial intelligence," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    4. Wang, Jianzhou & Gao, Jialu & Wei, Danxiang, 2022. "Electric load prediction based on a novel combined interval forecasting system," Applied Energy, Elsevier, vol. 322(C).
    5. Hongmei Zhao & Yang Xu & Wei-Chiang Hong & Yi Liang & Dandan Zou, 2021. "Smart Evaluation of Green Campus Sustainability Considering Energy Utilization," Sustainability, MDPI, vol. 13(14), pages 1-21, July.
    6. Jihoon Moon & Junhong Kim & Pilsung Kang & Eenjun Hwang, 2020. "Solving the Cold-Start Problem in Short-Term Load Forecasting Using Tree-Based Methods," Energies, MDPI, vol. 13(4), pages 1-37, February.
    7. Huan Guo & Haoyuan Kang & Yujie Xu & Mingzhi Zhao & Yilin Zhu & Hualiang Zhang & Haisheng Chen, 2023. "Review of Coupling Methods of Compressed Air Energy Storage Systems and Renewable Energy Resources," Energies, MDPI, vol. 16(12), pages 1-22, June.
    8. Kong, Xiangyu & Li, Chuang & Wang, Chengshan & Zhang, Yusen & Zhang, Jian, 2020. "Short-term electrical load forecasting based on error correction using dynamic mode decomposition," Applied Energy, Elsevier, vol. 261(C).
    9. Saima Akhtar & Sulman Shahzad & Asad Zaheer & Hafiz Sami Ullah & Heybet Kilic & Radomir Gono & Michał Jasiński & Zbigniew Leonowicz, 2023. "Short-Term Load Forecasting Models: A Review of Challenges, Progress, and the Road Ahead," Energies, MDPI, vol. 16(10), pages 1-29, May.
    10. Abhirup Khanna & Bhawna Yadav Lamba & Sapna Jain & Vadim Bolshev & Dmitry Budnikov & Vladimir Panchenko & Alexandr Smirnov, 2023. "Biodiesel Production from Jatropha: A Computational Approach by Means of Artificial Intelligence and Genetic Algorithm," Sustainability, MDPI, vol. 15(12), pages 1-33, June.
    11. Luo, X.J. & Oyedele, Lukumon O. & Ajayi, Anuoluwapo O. & Akinade, Olugbenga O. & Owolabi, Hakeem A. & Ahmed, Ashraf, 2020. "Feature extraction and genetic algorithm enhanced adaptive deep neural network for energy consumption prediction in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    12. Wu, Han & Liang, Yan & Heng, Jiani, 2023. "Pulse-diagnosis-inspired multi-feature extraction deep network for short-term electricity load forecasting," Applied Energy, Elsevier, vol. 339(C).
    13. Iftikhar Ahmad & Adil Sana & Manabu Kano & Izzat Iqbal Cheema & Brenno C. Menezes & Junaid Shahzad & Zahid Ullah & Muzammil Khan & Asad Habib, 2021. "Machine Learning Applications in Biofuels’ Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions," Energies, MDPI, vol. 14(16), pages 1-27, August.
    14. Ivana Kiprijanovska & Simon Stankoski & Igor Ilievski & Slobodan Jovanovski & Matjaž Gams & Hristijan Gjoreski, 2020. "HousEEC: Day-Ahead Household Electrical Energy Consumption Forecasting Using Deep Learning," Energies, MDPI, vol. 13(10), pages 1-29, May.
    15. Tao Wang & Sixuan Li & Wenyong Li & Quan Yuan & Jun Chen & Xiang Tang, 2023. "A Short-Term Parking Demand Prediction Framework Integrating Overall and Internal Information," Sustainability, MDPI, vol. 15(9), pages 1-25, April.
    16. Yan Lu & Xuan Liu & Yan Zhang & Zhiqiao Yang & Yunna Wu, 2023. "Investment Efficiency Assessment Model for Pumped Storage Power Plants Considering Grid Operation Demand under Fuzzy Environment: A Case Study in China," Sustainability, MDPI, vol. 15(11), pages 1-23, May.
    17. Nebojsa Bacanin & Catalin Stoean & Miodrag Zivkovic & Miomir Rakic & Roma Strulak-Wójcikiewicz & Ruxandra Stoean, 2023. "On the Benefits of Using Metaheuristics in the Hyperparameter Tuning of Deep Learning Models for Energy Load Forecasting," Energies, MDPI, vol. 16(3), pages 1-21, February.
    18. Noushabadi, Abolfazl Sajadi & Dashti, Amir & Raji, Mojtaba & Zarei, Alireza & Mohammadi, Amir H., 2020. "Estimation of cetane numbers of biodiesel and diesel oils using regression and PSO-ANFIS models," Renewable Energy, Elsevier, vol. 158(C), pages 465-473.
    19. Xie, Shuyi & Huang, Zimeng & Wu, Gang & Luo, Jinheng & Li, Lifeng & Ma, Weifeng & Wang, Bohong, 2024. "Combining precursor and Cloud Leaky noisy-OR logic gate Bayesian network for dynamic probability analysis of major accidents in the oil depots," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    20. Yuanzhuo Du & Kun Zhang & Qianzhi Shao & Zhe Chen, 2023. "A Short-Term Prediction Model of Wind Power with Outliers: An Integration of Long Short-Term Memory, Ensemble Empirical Mode Decomposition, and Sample Entropy," Sustainability, MDPI, vol. 15(7), pages 1-15, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:17:p:10991-:d:905341. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.