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Random decision forest (RDF) and crystal structure algorithm (CryStAl) for uncertainty consideration of RES & load demands with optimal design of hybrid CCHP systems

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  • Gopila, M.
  • Suresh, G.
  • Prasad, D.

Abstract

This manuscript proposes a multi-objective random development mode of hybrid Combined Cooling, Heating, And Power (CCHP) system, supply configuration, enclosing a turbine, cooler/heater, battery and storage tank, and photovoltaic/thermal collectors. The proposed optimal strategy is consolidation of Random Decision Forest (RDF) and Crystal Structure Algorithm (CryStAl), hence it is called RDF-CryStAl technique. The Power center models of power converters along with storage devices are create by assuming the characteristics of non-design elements. The annual value rate falls when system confidence levels drop and uncertainty is used. The yearly value savings rate is shown to be highly sensitive to the price of fossil-fuels as a result of the sensitivity analysis of economic-frontiers based on important economic factors. As a result, the inversion of star-collectors includes a robust impact of the turbine.

Suggested Citation

  • Gopila, M. & Suresh, G. & Prasad, D., 2023. "Random decision forest (RDF) and crystal structure algorithm (CryStAl) for uncertainty consideration of RES & load demands with optimal design of hybrid CCHP systems," Energy, Elsevier, vol. 282(C).
    • Handle: RePEc:eee:energy:v:282:y:2023:i:c:s0360544223019394
    DOI: 10.1016/j.energy.2023.128545
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    Cited by:

    1. Wenbo Zhao & Ling Fan, 2024. "Short-Term Load Forecasting Method for Industrial Buildings Based on Signal Decomposition and Composite Prediction Model," Sustainability, MDPI, vol. 16(6), pages 1-21, March.

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