首页> 外文会议>IEEE International Scientific Conference on Power and Electrical Engineering of Riga Technical University >IoT Solution Approach for Energy Consumption Reduction in Buildings: Part 4. Mathematical Model and Experiments for Cooling Energy Consumption
【24h】

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

机译:降低建筑物能耗的物联网解决方案方法:第4部分。冷却能耗的数学模型和实验

获取原文

摘要

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%.
机译:如今,可以使用各种IoT解决方案获取几乎实时的测量数据,这些解决方案可用于控制楼宇管理系统,例如供暖,通风,冷却设备(冷却器),照明。尽管如此,仍然有数量有限的解决方案可以通过使用每小时数据(例如,电力消耗,室温,湿度,CO2水平,热能,通风系统压力,室外气候数据)进行控制。本文涉及6R2C数学模型的开发,该模型使用从IoT传感器获得的实时数据,夏季需要冷却能量时获得的实际测量结果和实验测试结果。针对某些建筑物区域进行了测量和实验,该建筑物区域是建筑物中最耗电的HVAC系统的PN4通风区域,该区域也位于南侧,受太阳辐射的影响最大。使用简化的建模和输入数据方法,从8月8日至9月8日的每日消费量模型的CV(RMSE)估计值达到28.62%。在每月期间,平均能耗误差(单月)为0.14%。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号