TY - JOUR
AU - Mohd Aris, Suhaidi
AU - Dahlan, Nofri Yenita
AU - Mohd Nawi, Mohd Nasrun
AU - Ahmad Nizam, Tengku
AU - Tahir, Mohamad Zamhari
PY - 2015/11/05
Y2 - 2023/12/02
TI - QUANTIFYING ENERGY SAVINGS FOR RETROFIT CENTRALIZED HVAC SYSTEMS AT SELANGOR STATE SECRETARY COMPLEX
JF - Jurnal Teknologi
JA - Jurnal Teknologi
VL - 77
IS - 5
SE - Science and Engineering
DO - 10.11113/jt.v77.6125
UR - https://journals.utm.my/jurnalteknologi/article/view/6125
SP -
AB - <p class="Abstract"><span lang="EN-GB">Objective of this study is to estimate building energy saving at Bangunan Sultan Salahuddin Abdul Aziz Shah from a retrofit of Water Cooling Package Unit (WCPU) system. This research calculates energy savings as recommended by International Performance Measurement and Verification Protocol (IPMVP) using Option C-Whole Facility Measurement. In this study, the baseline period is defined from July 2012 to June 2013, the retrofit of WCPU was performed on July 2013 and the reporting period is from August 2013 to July 2014. The baseline energy use and the post retrofit energy use data are collected from utility bills. On the other hand, the energy governing factors other than the retrofit such as outdoor temperature or Cooling Degree Day (CDD), number of working days (NWD) and occupancy on the building are gathered corresponding to the pre-defined baseline and post-retrofit period. These non-retrofit energy governing factors are used to model adjusted baseline energy in calculating energy savings using regression analysis. </span><span lang="EN-US">Two types of energy saving analyses have been presented in the case study; 1) Single linear regression for each independent variable, 2) Multiple linear regression. Results show that number of occupancy has the highest coefficient regression, <em>R<sup>2</sup></em> followed by NWD and CDD. This indicates that occupancy has stronger correlation with the energy use in the building than NWD and CDD. Finding also shows that the <em>RÂ²</em> for multiple linear regression model are higher than single linear regression model. This shows the fact that more than one component are affecting the energy use in the building.</span></p>
ER -