Impacts of cooling load calculation uncertainties on the design optimization of building cooling systems

This paper focus on cooling load calculation of the meeting hall [4m*15m*7m] in the location of 8.55 north latitude, East longitude 39.27 and Altitude 1726 m elevation above sea level. The total building cooling load consists of inside design condition of building, outside design condition of building, consider building mater and wall facing to sun and etc.by categorized in to sensible and latent heat gain from ventilation, infiltration and occupants. From different Room heat gain component, the total heat load 21,301.66 w.

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Robust optimal design of building cooling systems considering cooling load uncertainty and equipment reliability

Applied Energy ◽

10.1016/j.apenergy.2015.08.070 ◽

2015 ◽

Vol 159 ◽

pp. 265-275 ◽

Cited By ~ 36

Author(s):

Wenjie Gang ◽

Shengwei Wang ◽

Fu Xiao ◽

Dian-ce Gao

Keyword(s):

Optimal Design ◽

Cooling Load ◽

Cooling Systems ◽

Load Uncertainty ◽

Equipment Reliability ◽

Robust Optimal Design ◽

Building Cooling

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An In Situ Experimental Method for the Development and Validation of Slat-Type Blind Models in Cooling Load Calculations

Journal of Solar Energy Engineering ◽

10.1115/1.2188530 ◽

2005 ◽

Vol 128 (2) ◽

pp. 189-198 ◽

Cited By ~ 7

Author(s):

Chanvit Chantrasrisalai ◽

Daniel E. Fisher

Keyword(s):

Measured Data ◽

Energy Calculation ◽

Cooling Load ◽

Experimental Uncertainty ◽

Experimental Facility ◽

Experimental Procedure ◽

Load Calculation ◽

Building Cooling ◽

Development And Validation

An in situ experimental procedure suitable for the development and validation of slat-type blind models used in building cooling load calculations is presented. Unique requirements of the experimental facility are presented, and measured data from the facility are compared with existing experimentally validated models. The experimental uncertainty associated with the measured solar transmittance is shown to be less than ±0.05, well within the range of accuracy required for the development of cooling load calculation procedures. The new procedure was used to validate the fenestration model in EnergyPlus, a heat balance based cooling load and energy calculation program.

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New design-day method for building cooling load calculation in China

Building Research & Information ◽

10.1080/09613218.2019.1648202 ◽

2019 ◽

Vol 47 (8) ◽

pp. 901-911

Author(s):

Xia Wu ◽

Zhe Tian ◽

Chengzhi Tian ◽

Yuanyuan Wang ◽

Jiaqing Li

Keyword(s):

Cooling Load ◽

Load Calculation ◽

Building Cooling

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ANALISIS BEBAN PENDINGINAN PADA COLD STORAGE UNTUK PENYIMPANAN DAGING

JURNAL ILMIAH TEKNIK MESIN ◽

10.33558/jitm.v7i1.1905 ◽

2019 ◽

Vol 7 (1) ◽

pp. 12-22

Author(s):

Ratu Mutia Fajarani ◽

Yopi Handoyo ◽

Raden Hengki Rahmanto

Keyword(s):

Experimental Method ◽

Cold Storage ◽

Freezing Point ◽

Cooling Load ◽

Internal Factors ◽

External Cooling ◽

Preservation Method ◽

Load Calculation ◽

Selection Of

Cooling is the best preservation method than others because the food that has been cooled will remain fresh and will not experience a change in taste, color and aroma, besides all the activities that cause decay will stop so that the cooled food will last longer. (Hartanto, 1984). With the proper cooling engine planning, it can help with spatial adjustments, adjustments to loading, estimation of the power to be used, and budget plans. That is what is commonly called the cooling load calculation. Calculation of cooling load needs to be carried out before planning. This is necessary because the magnitude of the pending load is very influential on the selection of the cooling engine so that the freezing point for preserving food can be accurate. Pendiginan burden is influenced by external and internal factors. With the experimental method, it is obtained the results of the external cooling load as the external cooling load is 11.6 kW, the inner cooling load is 138.8 kW and the performance work coefficient (COP) is 2.

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Combined Solar Heating and Cooling Systems: Simulation and Design Optimization

ASME 2008 2nd International Conference on Energy Sustainability, Volume 2 ◽

10.1115/es2008-54127 ◽

2008 ◽

Cited By ~ 2

Author(s):

Giovanni Nurzia ◽

Giuseppe Franchini ◽

Antonio Perdichizzi

Keyword(s):

Design Optimization ◽

Heat Pump ◽

Solar Heating ◽

Solar Irradiation ◽

Space Heating ◽

Thermal Source ◽

Absorption Chiller ◽

Cooling Systems ◽

Detailed Model ◽

Heating And Cooling

The deployment of solar driven air conditioning is a feasible target in all countries where high solar irradiation matches high cooling loads in buildings: the goal is to gradually replace compression chillers and reduce peak electricity demand during summer. Moreover, as solar thermal collectors are installed, solar cooling systems can be profitably employed during winter. In the present work a code has been implemented for the simulation and the design optimization of combined solar heating and cooling systems. The following system layout has been considered: in warm months the cooling demand is satisfied by means of an absorption chiller — driven by a solar collector field — and a reversible heat pump operating in series. A hot storage matches the variability of solar radiation, while a cold storage smoothes the non-stationarity of cooling demand. During winter, the reversible compression heat pump operates for space heating. Solar collectors are used as thermal source at the evaporator of the heat pump, increasing its coefficient of performance. The code, based on TRNSYS platform, is able to simulate the system throughout a year. Besides TRNSYS standard components a detailed model of the absorption chiller has been included, in order to accurately simulate its off-design operation. Using an optimization tool the size of each component is identified for a given space heating and cooling demand. The minimization of life cycle costs of the system has been chosen as the objective of the optimization. Results of a case study are presented and discussed for a solar heating and cooling plant in an office building. The optimization procedure has been carried out with simulations for a typical Northern Italy town (Alpine climate) and a typical Southern Italy town (Mediterranean climate).

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