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Fuzzy Efficient Energy Smart Home Management System for Renewable Energy Resources

Author

Listed:
  • Ronggang Zhang

    (Business School, Northwest University of Political Science and Law, Xi’an 710122, China)

  • Sathishkumar V E

    (Department of Information and Communication Engineering, Sunchon National University, Suncheon 13557, Korea)

  • R. Dinesh Jackson Samuel

    (Faculty of Technology, Design and Environment, Visual Artificial Intelligence Lab, Oxford Brookes University, Oxford OX3 0BP, UK)

Abstract

This article provides a fuzzy expert system for efficient energy smart home management systems (FES-EESHM), demand management, renewable energy management, energy storage, and microgrids. The suggested fuzzy expert framework is utilized to simplify designing smart microgrids with storage systems, renewable sources, and controllable loads on resources. Further, the fuzzy expert framework enhances energy and storage to utilize renewable energy and maximize the microgrid’s financial gain. Moreover, the fuzzy expert system utilizes insolation, electricity price, wind speed, and load energy controllably and unregulated as input variables to enable energy management. It uses input variables including insolation, electrical quality, wind, and the power of uncontrollable and controllable loads to allow energy management. Furthermore, these input data can be calculated, imported, or predicted directly via grid measurement using any prediction process. In this paper, the input variables are fuzzified, a series of rules are specified by the expert system, and the output is de-fuzzified. The findings of the expert program are discussed to explain how to handle microgrid power consumption and production. However, the decisions on energy generated, controllable loads, and own consumption are based on three outputs. The first production is for processing, selling, or consuming the energy produced. The second output is used for controlling the load. The third result shows how to produce for prosumer’s use. The expert method can be checked via the hourly input of variable values. Finally, to confirm the findings, the method suggested is compared to other available approaches.

Suggested Citation

  • Ronggang Zhang & Sathishkumar V E & R. Dinesh Jackson Samuel, 2020. "Fuzzy Efficient Energy Smart Home Management System for Renewable Energy Resources," Sustainability, MDPI, vol. 12(8), pages 1-14, April.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:8:p:3115-:d:344900
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    References listed on IDEAS

    as
    1. Arcos-Aviles, Diego & Pascual, Julio & Guinjoan, Francesc & Marroyo, Luis & Sanchis, Pablo & Marietta, Martin P., 2017. "Low complexity energy management strategy for grid profile smoothing of a residential grid-connected microgrid using generation and demand forecasting," Applied Energy, Elsevier, vol. 205(C), pages 69-84.
    2. Ciabattoni, Lucio & Grisostomi, Massimo & Ippoliti, Gianluca & Longhi, Sauro, 2014. "Fuzzy logic home energy consumption modeling for residential photovoltaic plant sizing in the new Italian scenario," Energy, Elsevier, vol. 74(C), pages 359-367.
    3. Mario Collotta & Giovanni Pau, 2015. "A Solution Based on Bluetooth Low Energy for Smart Home Energy Management," Energies, MDPI, vol. 8(10), pages 1-23, October.
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    Cited by:

    1. Yang Zhimao, 2023. "RETRACTED ARTICLE: Research on the influencing factors of living energy consumption and carbon emissions based on spatiotemporal model," Journal of Combinatorial Optimization, Springer, vol. 45(1), pages 1-20, January.
    2. Amrutha Raju Battula & Sandeep Vuddanti & Surender Reddy Salkuti, 2021. "Review of Energy Management System Approaches in Microgrids," Energies, MDPI, vol. 14(17), pages 1-32, September.
    3. Mingming Wen & Changshi Zhou & Mamonov Konstantin, 2023. "Deep Neural Network for Predicting Changing Market Demands in the Energy Sector for a Sustainable Economy," Energies, MDPI, vol. 16(5), pages 1-17, March.
    4. Xinping Wang & Cheng Zhang & Jun Deng & Chang Su & Zhenzhe Gao, 2022. "Analysis of Factors Influencing Miners’ Unsafe Behaviors in Intelligent Mines using a Novel Hybrid MCDM Model," IJERPH, MDPI, vol. 19(12), pages 1-30, June.

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