IoT Solution Approach for Energy Consumption Reduction in Buildings: Part 4. Mathematical Model and Experiments for Cooling Energy Consumption

A. Avotiņš, Andrejs Podgornovs, P. Apse-Apsitis, Armands Senfelds, E. Dzelzītis, Kristaps Zadeiks
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引用次数: 1

Abstract

Nowadays it is possible to obtain almost real-time measurement data using various IoT solutions, which can be used in order to control building management systems like heating, ventilation, cooling equipment (chiller), lighting. Nevertheless there are limited number of solutions allowing to control it by using hourly data (like electrical power consumption, room temperatures, humidity, CO2 levels, heat energy, ventilation system pressures, outdoor climate data). This paper deals with 6R2C mathematical model development, that uses real-time data obtained from IoT sensors, practical measurements and experimental testing results achieved during summer period, when the cooling energy is needed. Measurements and experiments were conducted for certain building zone, which is PN4 ventilation zone for the most electrical energy consuming HVAC system of the building, located also in the south side and having most impact by the sun radiation. Using simplified modeling and input data approach, CV(RMSE) estimation of the model for daily consumption for the period from 8 August to 8 September resulted in a value of 28.62%. In monthly period average energy consumption error (single month) is 0.14%.
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降低建筑能耗的物联网解决方案:第4部分。制冷能耗的数学模型与实验
如今,使用各种物联网解决方案可以获得几乎实时的测量数据,这些数据可用于控制建筑管理系统,如供暖、通风、冷却设备(冷却器)、照明。然而,通过使用每小时的数据(如电力消耗、室温、湿度、二氧化碳水平、热能、通风系统压力、室外气候数据)来控制它的解决方案数量有限。本文涉及6R2C数学模型的开发,该模型使用了从物联网传感器获得的实时数据,实际测量和夏季需要冷却能量的实验测试结果。对某建筑区域进行了测量和实验,该区域为该建筑暖通空调系统能耗最大的PN4通风区域,同样位于南侧,受太阳辐射影响最大。采用简化建模和输入数据法,对8月8日至9月8日期间的日均消费量模型进行CV(RMSE)估计,结果为28.62%。月度平均能耗误差(单月)为0.14%。
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