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基于多能匹配供暖的内蒙古西部草原民居综合因素分析

Residential Heating with Multi-Source in Western Inner Mongolia:A Simulation and Experimental Study
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摘要 通过对内蒙古西部172户草原民居进行调研,并从中挑选出1户典型草原民居,利用Design Builder能耗模拟软件进行模拟,结果表明:在供暖周期内,多能匹配供暖比传统供暖在主卧室内平均温度提高约5.465℃,次卧提高约5.107℃,客厅提高约5.105℃,而室内热舒适情况,利用PMV平均值进行比较,主卧评价为冷到稍热,次卧为寒冷到稍冷以及客厅为稍冷到稍热。在能耗上可节约4 210.91kWh,可节省1 768.58元。在经济性方面,虽然初始投资成本比较高,但折算成25年内的每年成本以后,可以节约674.87元,而25年内节约共计16 871.75元,从而证实了多能匹配供暖系统的可行性、节能性和经济性,本文研究为该地区供暖方式、改善室内环境和优化能源结构提供一定的参考。 We addressed the energy consumption in heating of a typical three-bedroom house in western Inner Mongolia by solid fuel-burning via adobe-bed and with locally abundant solar/wind energies,characterized by windmill generator,advanced materials,solar-panels and electrical heaters.A typical residential heating was modeled,simulated with Design-Builder software and measured.The simulated results show that when it comes to comfortable residential heating in winter,the multi-source technique outperforms the conventional adobe-bed method.For instance,the average temperature,in master/second bedrooms and sitting-room increases by 5.465℃,5.107℃ and 5.105℃,respectively;the annual energy consumption decreases by 4210.91 kWh;and the investment recovery period was estimated to be^25 years.The simulated and measured results were in good agreement.We suggest that the multi-source heating be of much technological interest in residential house construction in developing western Inner Mongolia.
作者 金国辉 梁娜飞 丁超 王振华 Jin Guohui;Liang Nafei;Ding Chao;Wang Zhenhua(School of Civil Engineering,Inner Mongolia University of Science and Technology,Baotou 014010,China;State Key Laboratory of Green Building in Western China,Xian University of Architecture&Technology,Xian 710055,China)
出处 《真空科学与技术学报》 EI CAS CSCD 北大核心 2020年第8期768-774,共7页 Chinese Journal of Vacuum Science and Technology
基金 国家自然科学基金项目(51768053) 内蒙古自然科学基金项目(2019MS05007 2017MS(LH)0532) 西部绿色建筑国家重点实验室培育基地开放研究基金项目(编号:LSKF201803)。
关键词 内蒙古西部草原民居 多能匹配 供暖模式 综合因素分析 Inner Mongolia western grassland houses Multiple matching Heating mode Comprehensive factor analysis
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