The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLVI-4/W6-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-4/W6-2021, 289–294, 2021
https://doi.org/10.5194/isprs-archives-XLVI-4-W6-2021-289-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-4/W6-2021, 289–294, 2021
https://doi.org/10.5194/isprs-archives-XLVI-4-W6-2021-289-2021

  18 Nov 2021

18 Nov 2021

GIS-BASED THERMAL LOAD ESTIMATION OF BUILDINGS IN THE NATIONAL SCIENCE COMPLEX, UP DILIMAN

C. A. Tatlonghari1 and J. A. Principe2 C. A. Tatlonghari and J. A. Principe
  • 1National Graduate School of Engineering, University of the Philippines Diliman, Quezon City, Philippines
  • 2Dept. of Geodetic Engineering, University of the Philippines Diliman, Quezon City, Philippines

Keywords: Thermal load, 3D visualization, DTM, smart city, sustainability

Abstract. Building thermal load is the energy exhausted to maintain a specific indoor temperature in comparison to the outdoor temperature. Majority of this energy makes use of a considerable amount of fossil fuels which contributes to greenhouse gases emission leading to global warming. Thermal load estimation of buildings allows people to identify infrastructures in need for retrofit for a more sustainable and smart urban management. This paper presents a small-scale study to estimate the thermal cooling load of fourteen (14) buildings in the National Science Complex of the University of the Philippines Diliman. Results of the annual cooling load calculation for the year 2020 was reported with an estimated lowest cooling load of 1,618 kW for the Philippine Atmospheric, Geophysical and Astronomical Services Administration (PAGASA) observatory and the highest cooling load of 13,484 kW for the Institute of Mathematics. The values calculated was an overestimation as the entire building was set up as a homogenous cold room without any windows or doors. For future work, it is recommended that input data be supplemented with digital surface model (DSM) and triangulated irregular network (TIN) raster data derived from Light Detection and Ranging (LiDAR) to not only categorize but assign specific values over each building group of the study area.