Energy-saving optimization of the parallel chillers system based on a multi-strategy improved sparrow search...

Area of Science:

• Engineering
• Computer Science
• Energy Systems

Background:

• Parallel chiller systems represent a significant portion (25-40%) of a building's total energy consumption.
• Effective load distribution in chiller systems is crucial for energy conservation and mitigating global warming impacts.

Purpose of the Study:

• To develop an optimized approach for the optimal chiller loading (OCL) problem.
• To enhance energy savings in buildings through efficient parallel chiller systems management.

Main Methods:

• A novel Multi-Strategy Improved Sparrow Search Algorithm (MSSA) was developed.
• MSSA integrates Sine chaotic map, Levy flight, and Cauchy variation to improve search capabilities and avoid local optima.
• The algorithm's performance was evaluated using 9 benchmark functions and two real-world case studies.

Main Results:

• MSSA demonstrated superior performance compared to Particle Swarm Optimization (PSO), Harris Hawks Optimization (HHO), Artificial Rabbit Optimization (ARO), and the basic Sparrow Search Algorithm (SSA).
• The algorithm effectively optimized chiller loading in practical scenarios, outperforming existing methods.
• Validation through benchmark functions confirmed MSSA's enhanced global and local search capacities.

Conclusions:

• The Multi-Strategy Improved Sparrow Search Algorithm (MSSA) offers a promising and effective solution for the optimal chiller loading problem.
• MSSA contributes to significant energy savings in buildings by optimizing parallel chiller system operations.
• This research highlights the potential of advanced metaheuristic algorithms in addressing complex energy management challenges.