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Optimization of Distributed Energy Resources Operation in Green Buildings Environment.

Safdar Ali1, Khizar Hayat1, Ibrar Hussain1,2

  • 1Department of Software Engineering, The University of Lahore, Main Campus, Lahore 54590, Pakistan.

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Summary

This study introduces a Power Management and Control (PMC) system that optimizes energy use and occupant comfort. The PMC system effectively reduces energy consumption while maintaining or improving the occupant comfort index (OCI).

Keywords:
energy managementenergy resourcesevolutionary algorithmsgreen buildingsoccupants comfort indexprediction

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Area of Science:

  • Energy Management Systems
  • Artificial Intelligence in Building Automation
  • Sustainable Energy Solutions

Background:

  • Effective energy management is crucial for lifestyle improvements but faces challenges with limited, costly resources.
  • Existing energy management solutions often focus on either energy reduction or occupant comfort, not both.
  • The multi-objective nature of energy management requires balancing reduced consumption with maintained occupant comfort.

Purpose of the Study:

  • To propose a novel energy control system, Power Management and Control (PMC), for green environments.
  • To address the multi-objective challenge of reducing energy consumption while preserving occupant comfort.
  • To develop a hybrid optimization framework combining Genetic Algorithm (GA) and Particle Swarm Optimization (PSO).

Main Methods:

  • Implementation of a Power Management and Control (PMC) system utilizing hybrid energy optimization, energy prediction, and multi-preprocessing.
  • Fusion of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for enhanced energy management.
  • Simulation-based validation controlling indoor actuators (fan, light, AC, boiler) to assess performance.

Main Results:

  • The PMC framework achieved a superior Occupant Comfort Index (OCI) compared to ABCKB, GAP, SOHP, and PSO frameworks.
  • PMC demonstrated comparable OCI to the AEO framework but with significantly lower energy consumption.
  • PMC achieved an ideal OCI of 1, outperforming the FA-GA model's OCI of 0.98, and consumed less energy than ABCKB, GAP, PSO, and AEO.

Conclusions:

  • The proposed PMC framework effectively reduces energy utilization and enhances the Occupant Comfort Index (OCI).
  • PMC offers a robust solution for multi-objective energy management in green environments.
  • The system's performance is validated through simulation, demonstrating its capability to control indoor environments.