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Fuzzy rough set based energy management system for self-sustainable smart city

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  • Sharma, Sumedha
  • Dua, Amit
  • Singh, Mukesh
  • Kumar, Neeraj
  • Prakash, Surya

Abstract

A smart city (SC) is a modernized urban community, which incorporates information and communication technology (ICT) into the existing physical infrastructure. It offers reliable and uninterrupted services such as-intelligent transport and energy conservation to its residents. Among the various services offered, a continuous and widespread supply of electricity draws the maximum attention. Moreover, a substantial amount of this electricity is utilized by heating and cooling loads, making them significant contributors of global warming. Keeping focus on these points, a SC is modeled in this paper, wherein electricity consumption is minimized by modifying the traditional heating and cooling system. The proposed system collects solar heat and recovers industrial waste heat to deliver heating and cooling services to the SC residents. For the purpose of storing the heat energy, a seasonal thermal energy storage (STES) system has been proposed. The proposed STES system supplies stored thermal energy to heat-driven cooling and heating equipments. Moreover, central energy management system (CEMS) is also modeled for monitoring, regulating and controlling the flow of thermal energy. The designed CEMS consists of a Fuzzy rough set controller for scheduling heat to cater the instantaneous thermal energy requirements. Fuzzy rough set has been used because it eliminates the issues of vagueness, uncertainty, and ensures efficient real-time computations. Results depict that the heat energy obtained from solar thermal collectors and industrial wastes is able to meet the requirements of the SC after scheduling it using Fuzzy rough set algorithm.

Suggested Citation

  • Sharma, Sumedha & Dua, Amit & Singh, Mukesh & Kumar, Neeraj & Prakash, Surya, 2018. "Fuzzy rough set based energy management system for self-sustainable smart city," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3633-3644.
    • Handle: RePEc:eee:rensus:v:82:y:2018:i:p3:p:3633-3644
    DOI: 10.1016/j.rser.2017.10.099
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    References listed on IDEAS

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    6. Gung, Roger R. & Huang, Chun-Che & Hung, Wen-I & Fang, Yu-Jie, 2020. "The use of hybrid analytics to establish effective strategies for household energy conservation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).

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