Multi‐objective optimisation of hydroelectric PMSG considering water‐level variation

Gao, Jian; Dai, Litao; Zhang, Wenjuan; Huang, Shoudao

doi:10.1049/joe.2019.0100

Cited by 1 publication

(1 citation statement)

References 19 publications

(28 reference statements)

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“…Tian Y. used a genetic algorithm to optimize office buildings in cold regions, studying the relationship between design parameters, building energy consumption, and light-thermal comfort [48]. Gao Y. and others built an optimization design framework for rural residences in the north, aiming to reduce heating energy consumption and improve thermal comfort, and analyzed design solutions for L-shaped and U-shaped rural houses [49]. Xu K. and Wang Y. used performance objectives such as thermal climate index, heat radiation, and sky openness, employing Ladybug and Honeybee simulation plugins and the Wallacei genetic optimization plugin, to optimize the architectural layout of university campuses [50].…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization for Winter Heating Retrofit in Rural Houses of Cold Regions: A Case Study in the Wusu Area

Xi,

Gao,

Hou

et al. 2024

Applied Sciences

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In regions of China experiencing severe cold, the duration of the winter heating season significantly contributes to elevated heating energy consumption in rural dwellings. This study focuses on typical brick-and-concrete rural homes in the Wusu area. Utilizing the Rhino–Grasshopper parametric modeling platform, it aims to minimize heating-related carbon emissions and the overall costs associated with retrofitting. The approach involves improving the insulation properties of the building envelope to reduce energy requirements. Additionally, the study incorporates solar photovoltaic systems atop rural homes, building upon low-carbon, passive, energy-efficient design principles. By examining the influence of various factors on rural housing energy consumption, the research employs the entropy weight method to identify the most effective design solutions. The goal is to explore strategies for the energy-efficient retrofitting of rural dwellings in areas faced with harsh winter conditions, aligning with the objectives and preferences of Applied Sciences. The simulation results reveal the following: (1). In comparison with the baseline scenario, 42.2% of the optimized solutions within the Pareto frontier satisfy the current standards for 75% energy savings in energy-efficient residential design. (2). The lowest recorded thermal consumption index for the buildings can reach 12.427 W/m2, at which point the rate of energy savings is elevated to 79.5%. (3). Within the solutions identified by the Pareto frontier, 80% exhibit initial investments that are lower than the cost savings over the lifecycle due to reduced energy consumption (dCg < 0), demonstrating the economic feasibility of the proposed retrofitting strategies.

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Section: Introductionmentioning

confidence: 99%