Systematic review for determining the design associated to green electrical building in commercial sites

Felix Eduardo Ramírez Huamán, Mario Quispe Bayona, R. Velásquez
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Abstract

In this paper, the design criteria for green buildings have been analyzed in order to reduce the industrial electrical energy consumption, through the analysis of the research articles in Science Direct, SCOPUS and IEEE, with the Population, intervention, comparison, outcome and context (PICOC) methodology for the systematic review. Our findings have demonstrated the use of BUSBAR type conductors, the inclusion of refrigeration, heating and energy (CCHP), as an economic and ecological technology; on the other hand, the proliferation of smart cities and sensors with a better understand occupant behavior and energy use patterns on an urban scale, besides, the use of LED lighting in common areas helps with high luminous efficiency and long useful life compared to other types of light fixtures; finally, the references applied to urban cities are 19 from 165 papers from 2015 to 2021. It should be used with a non-linear programming model (MINLP) for a combined CCHP system together with renewable energy, so that the rate of CO2 emissions can be reduced suddenly, besides, the PV solar panels allows with the BESS to achieve energy savings with exogenous artificial neural network.
为确定商业场所中绿色电气建筑设计而进行的系统评审
本文通过对Science Direct、SCOPUS和IEEE上的研究文章的分析,采用人口、干预、比较、结果和背景(PICOC)方法进行系统综述,分析了绿色建筑的设计标准,以降低工业电能消耗。我们的研究结果表明,使用BUSBAR型导体,包括制冷,加热和能源(CCHP),作为一种经济和生态技术;另一方面,智能城市和传感器的普及,可以更好地了解城市尺度上的居住者行为和能源使用模式,此外,与其他类型的灯具相比,在公共区域使用LED照明有助于提高发光效率和延长使用寿命;最后,在2015 - 2021年的165篇论文中,适用于城市的参考文献为19篇。对于与可再生能源相结合的热电联产系统,应采用非线性规划模型(MINLP),使CO2的排放速率突然降低,同时光伏太阳能板可以与BESS一起通过外生人工神经网络实现节能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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