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Development of an integrated Grey–fuzzy-based electricity management system for enterprises

Author

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  • Yao, Albert W.L.
  • Chi, S.C.
  • Chen, C.K.

Abstract

The goal of this project is to develop a PC-based electricity management system for our university. This proposed demand control electricity management system is embedded with an intelligent predictor and controller, in order to forecast time-variant electric demand online and to manage the electric facilities real-time. This paper presents the design of a fuzzy logic-based electric controller and concludes with a discussion of this study. The proposed hybrid PC web-based intelligent electric demand control system is able to provide an efficient means to control and manage the use of electricity for enterprises.

Suggested Citation

  • Yao, Albert W.L. & Chi, S.C. & Chen, C.K., 2005. "Development of an integrated Grey–fuzzy-based electricity management system for enterprises," Energy, Elsevier, vol. 30(15), pages 2759-2771.
    • Handle: RePEc:eee:energy:v:30:y:2005:i:15:p:2759-2771
    DOI: 10.1016/j.energy.2005.02.001
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    Cited by:

    1. An, Ning & Zhao, Weigang & Wang, Jianzhou & Shang, Duo & Zhao, Erdong, 2013. "Using multi-output feedforward neural network with empirical mode decomposition based signal filtering for electricity demand forecasting," Energy, Elsevier, vol. 49(C), pages 279-288.
    2. Akay, Diyar & Atak, Mehmet, 2007. "Grey prediction with rolling mechanism for electricity demand forecasting of Turkey," Energy, Elsevier, vol. 32(9), pages 1670-1675.
    3. Chen, Chun-I, 2008. "Application of the novel nonlinear grey Bernoulli model for forecasting unemployment rate," Chaos, Solitons & Fractals, Elsevier, vol. 37(1), pages 278-287.
    4. Pao, Hsiao-Tien & Fu, Hsin-Chia & Tseng, Cheng-Lung, 2012. "Forecasting of CO2 emissions, energy consumption and economic growth in China using an improved grey model," Energy, Elsevier, vol. 40(1), pages 400-409.
    5. Shaikh, Faheemullah & Ji, Qiang & Shaikh, Pervez Hameed & Mirjat, Nayyar Hussain & Uqaili, Muhammad Aslam, 2017. "Forecasting China’s natural gas demand based on optimised nonlinear grey models," Energy, Elsevier, vol. 140(P1), pages 941-951.
    6. Pao, Hsiao-Tien & Tsai, Chung-Ming, 2011. "Modeling and forecasting the CO2 emissions, energy consumption, and economic growth in Brazil," Energy, Elsevier, vol. 36(5), pages 2450-2458.
    7. Zahedi, Gholamreza & Azizi, Saeed & Bahadori, Alireza & Elkamel, Ali & Wan Alwi, Sharifah R., 2013. "Electricity demand estimation using an adaptive neuro-fuzzy network: A case study from the Ontario province – Canada," Energy, Elsevier, vol. 49(C), pages 323-328.

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