Author:
Abidin Abdul Murad Zainal,Wajid Norhayati Mat,Fadzil Faiz,Omar Samsiah,Kamaruzzaman Syahrul Nizam,Mat Nik Elyna Myeda Nik,Azlan Anissa
Abstract
Abstract
The global increase in carbon emission from buildings is driving greater urgency towards carbon neutrality. A number of strategies have been developed worldwide that include energy management and building-integrated photovoltaics but these are implemented independently. There is a need for integrated approach towards better energy monitoring and benchmarking. The aim of the research is to develop an electronic energy benchmarking system (e-EBS) for public buildings in Malaysia as dashboard on energy consumption, carbon footprint, and green rating score with strategies for improvement. This is done by integrating energy auditing with the Penarafan Hijau JKR (pHJKR) building rating scheme, and the JKR Energy Online System (JENOSYS). The benchmarking system would analyse energy data and give the rating with recommended energy-saving strategies for building owners. The tool for analysis and benchmarking was selected by comparing the coefficient of determination (R2) of regression model with multi-layer perceptron, a type of neural network. The neural network model was found to have a slightly higher sensitivity level (R2=0.95) compared to the regression model (R2=0.93) and selected to develop a standard baseline of energy consumption for buildings in e-EBS. The following step is the validation of e-EBS by creating more energy benchmarks based on building category.