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Design and Development of Optimal and Deep-Learning-Based Demand Response Technologies for Residential Hybrid Renewable Energy Management System

Author

Listed:
  • Murugaperumal Krishnamoorthy

    (Department of EEE, Vardhaman College of Engineering, Hyderabad 501218, India)

  • P. Ajay-D-Vimal Raj

    (Department of EEE, Puducherry Technological University, Puducherry 605014, India)

  • N. P. Subramaniam

    (Department of EEE, Puducherry Technological University, Puducherry 605014, India)

  • M. Sudhakaran

    (Department of EEE, Puducherry Technological University, Puducherry 605014, India)

  • Arulselvi Ramasamy

    (Department of CSE, Vardhaman College of Engineering, Hyderabad 501218, India)

Abstract

The principal goal of this study is to conduct a techno-economic analysis of hybrid energy generation designs for residential-form houses in urban areas. Various possibilities for a form house electrification system are created and simulated in order to determine an optimum ideal configuration for meeting residential load demand with an increase in energy capacity and minimal investment. Using NREL’s HOMER optimization tool, a case-study-based virtual HRE model is developed. Pre-assessment data and relevant operation constraints are used to build the system’s objective functions. The instantaneous energy balance algorithm technique is used to solve the multi-objective function. The overall optimization procedure is sandwiched between two supporting advanced approaches, pre- and post-operations. The development of an optimal techno-economic hybrid energy generation system for the smooth fulfillment of urban load demand is aided by novel deep belief network (NDBN)-based pre-stage load demand predictions and an analysis of the necessary demand side management (DSM)practicing code for utility efficiency improvements in post-stage simulations.

Suggested Citation

  • Murugaperumal Krishnamoorthy & P. Ajay-D-Vimal Raj & N. P. Subramaniam & M. Sudhakaran & Arulselvi Ramasamy, 2023. "Design and Development of Optimal and Deep-Learning-Based Demand Response Technologies for Residential Hybrid Renewable Energy Management System," Sustainability, MDPI, vol. 15(18), pages 1-26, September.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:18:p:13773-:d:1240684
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    References listed on IDEAS

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