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Providing an Expert System to Manage Energy Consumption in Small‐ and Medium‐Sized Industries Using Soft Computing

Author

Listed:
  • Iman Koravand
  • Mohammad Reza Lotfi
  • Seyed Ahmad Shayannia

Abstract

The increasing growth of electricity consumption, the need for its continuous supply, and the impossibility of storing this vital energy are among the primary and essential reasons for preventing any interruption in supply and proper planning regarding energy consumption management, especially in industry. The innovation of this research is the design of a decision‐making system to help decision‐making in energy management in small‐ and medium‐sized industries using the knowledge of experts in small‐ and medium‐sized industries and the electricity industry in the field of energy consumption management and extracting relevant expertise rules to create a knowledge base. Therefore, the main advantage of the research is to provide an expert system for the correct management of energy consumption in small and medium industries using soft computing. Another advantage of the research is the use of uncertain decision‐making variables in this field; instead of using words and linguistic restrictions in this research, modeling them in the system using the principles of fuzzy logic is used. The main objectives of this research include determining the effective variables in determining the amount of energy production for small and medium industries, explaining the existing tacit knowledge and turning it into explicit knowledge, and providing an expert system for managing energy consumption in this field. Based on the results of field studies and the research literature, effective variables in determining the amount of electricity generation were identified. By extracting the knowledge of industry experts, the tacit knowledge available as explicit knowledge in the storage knowledge base and the system was designed to manage electrical charge consumption. One of the achievements of this system is its creation and measurement using various data and results obtained from small and medium industries. Finally, the results obtained from the expert system in comparing the output of expert opinions and the system’s performance in managing the amount of energy production showed that the usefulness of the expert system was designed. The system uses the knowledge base to make decisions such as an expert human. According to the main results of the research based on comparing the performance of the system with the Bayesian inference networks, it showed that there is an acceptable correlation between the data and the prediction system. Also, to evaluate the acquisition of data using a statistical test, it has been determined that the total number of blackout hours in different areas of Tehran in the base year was more than the current year.

Suggested Citation

  • Iman Koravand & Mohammad Reza Lotfi & Seyed Ahmad Shayannia, 2022. "Providing an Expert System to Manage Energy Consumption in Small‐ and Medium‐Sized Industries Using Soft Computing," Complexity, John Wiley & Sons, vol. 2022(1).
    • Handle: RePEc:wly:complx:v:2022:y:2022:i:1:n:1996637
    DOI: 10.1155/2022/1996637
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    References listed on IDEAS

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    1. Faham Tahmasebinia & Ruifeng Jiang & Samad Sepasgozar & Jinlin Wei & Yilin Ding & Hongyi Ma, 2022. "Using Regression Model to Develop Green Building Energy Simulation by BIM Tools," Sustainability, MDPI, vol. 14(10), pages 1-25, May.
    2. Rouhollah Sohrabi & Koorosh Pouri & Majid Sabk Ara & Sayyed Mohammadreza Davoodi & Esmaeel Afzoon & Adel Pourghader Chobar & Alireza Goli, 2021. "Applying Sustainable Development to Economic Challenges of Small and Medium Enterprises after Implementation of Targeted Subsidies in Iran," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-9, November.
    3. Fan, Cheng & Sun, Yongjun & Zhao, Yang & Song, Mengjie & Wang, Jiayuan, 2019. "Deep learning-based feature engineering methods for improved building energy prediction," Applied Energy, Elsevier, vol. 240(C), pages 35-45.
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