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Development of load calculation algorithm for large space building based on RTS method

  • Sungkyunkwan University

Research output: Contribution to journal › Conference article › peer-review

Abstract

Since a large space building has distinguished characteristics, to analyse and predict the peak load these should be considered. The existed building energy simulation cannot reflect the characteristics of large space building, especially vertical temperature distribution. Therefore, to analyse the large space building, it is inevitably used the methods which requires high knowledge skills and economic cost. The goal of this research is suggesting the peak load calculation algorithm for a large space building. With RTS(Radiant Time Series) and simplified model for predicting vertical temperature distribution, the algorithm is expected to show highly applicability with minimum input data and consisted with clear calculation process. The calculation process is composed with three steps. 1) calculation for preparing initial condition, 2) calculation of air movement, 3) calculating peak load for occupied space. With the Energyplus applying airflow network, the result of algorithm is assessed. The predicted temperature and peak load for an occupied zone at each cases are compared. The result applying proposed algorithm shows 33.64 ?C at 15 hour and the peak load is 50.92 kW. In case of applying Energy plus, 35.59 ?C, 63.93 kW at 16 hour is predicted. For the further research, vertical temperature measurements in large space building will be proceeded. By comparing the measured vertical temperature distribution and predicted temperature in a large space building, the algorithm will be assessed.

Original languageEnglish
Article number072059
JournalIOP Conference Series: Materials Science and Engineering
Volume609
Issue number7
DOIs
StatePublished - 23 Oct 2019
Event10th International Conference on Indoor Air Quality, Ventilation and Energy Conservation in Buildings, IAQVEC 2019 - Bari, Italy
Duration: 5 Sep 2019 → 7 Sep 2019

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

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