Development of a Heuristic Control Algorithm for Improving Energy Efficiency in Commercial Refrigerated Systems without Indoor Information

Kwon, Ji-Hoon; Kim, Man-Ho; Lee, Ju-Kyoung; Kim, Hyeong-Jun; Min, Seon-Gyu; Lee, Suk

doi:10.3390/electronics12224678

Cited by 1 publication

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References 15 publications

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“…In recent years, with advancements in computer technology, heuristic algorithms inspired by biology, physics, and artificial intelligence have seen widespread development and application [4], especially genetic algorithms. Genetic algorithms adhere to the fundamental principle of "natural selection, survival of the fittest".…”

Section: Introductionmentioning

confidence: 99%

Reservoir Optimization Scheduling Driven by Knowledge Graphs

Tang,

Feng,

Zhou

2024

Electronics

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As global climate change intensifies, the challenges of water scarcity and flood disasters become increasingly severe. This severity makes efficient reservoir scheduling management crucial for the rational utilization of water resources. Due to the diverse topological structures and varying objectives of different watersheds, existing optimization models and algorithms are typically applicable only to specific watershed environments. This specificity results in a “one watershed, one model” limitation. Consequently, optimization of different watersheds usually requires manual reconstruction of models and algorithms. This process is not only time-consuming but also limits the versatility and flexibility of the algorithms. To address this issue, this paper proposes a knowledge graph-driven method for reservoir optimization scheduling. By improving genetic algorithms, this method allows for the automatic construction of optimization models tailored to specific watershed characteristics based on knowledge graphs. This approach reduces the dependency of the optimization model on manual modeling. It also integrates hydrodynamic simulations within the watershed to ensure the effectiveness and practicality of the genetic algorithms. Furthermore, this paper has developed an algorithm that directly converts optimized reservoir outflow into actionable dispatch instructions. This method has been applied in the Pihe River Basin, optimizing flood control and resource management strategies according to different seasonal demands. It demonstrates high flexibility and effectiveness under varying hydrological conditions, significantly enhancing the operational efficiency of reservoir management.

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Section: Introductionmentioning

confidence: 99%