Computer Sciences Commons^™

Machine Learning-Enhanced All-Photovoltaic Blended Systems For Energy-Efficient Sustainable Buildings, Mohammad Nur-E-Alam, Kazi Z. Mostofa, Boon K. Yap, Mohammad K. Basher, Mohammad A. Islam, Mikhail Vasiliev, Manzoore E. M. Soudagar, Narottam Das, Tiong S. Kiong

Research outputs 2022 to 2026

The focus of this work is on the optimization of an all-photovoltaic hybrid power generation systems for energy-efficient and sustainable buildings, aiming for net-zero emissions. This research proposes a hybrid approach combining conventional solar panels with advanced solar window systems and building integrated photovoltaic (BIPV) systems. By analyzing the meteorological data and using the simulation models, we predict energy outputs for different cities such as Kuala Lumpur, Sydney, Toronto, Auckland, Cape Town, Riyadh, and Kuwait City. Although there are long payback times, our simulations demonstrate that the proposed all-PV blended system can meet the energy needs of modern buildings (up …

Accuracy Of Spectral Indices Assessing Fire Severity Utilizing Maximum And Minimum Pixel Values, Jarrad Mckercher, David Blake, Eddie Van Etten

Research Datasets

This data set contains all Spectral Indices created using Google Earth Engine through Google Collaborate. 16 Spectral Indices were created that utilise different image collection and pixel value parameters to map the burn severity of the 2021 Wooroloo Bushfire.

The Application Of Data Mining Techniques To Interrogate Western Australian Water Catchment Data Sets, Ajdin Sehovic

Theses : Honours

Current environmental challenges such as increasing dry land salinity, waterlogging, eutrophication and high nutrient runoff in south western regions of Western Australia may have both cultural and environmental implications in the near future. Advances in computer science disciplines, more specifically, data mining techniques and geographic information services provide the means to be able to conduct longitudinal climate studies to predict changes in the Water catchment areas of Western Australia.

The research proposes to utilise existing spatial data mining techniques in conjunction of modern open-source geospatial tools to interpret trends in Western Australian water catchment land use. This will be achieved …

Interrogation Of Water Catchment Data Sets Using Data Mining Techniques, Ajdin Sehovic, Leisa Armstrong, Dean Diepeveen

Research outputs pre 2011

Current environmental challenges such as increasing dry land salinity, water logging, eutrophication and high nutrient runoff in south western regions of Western Australia (WA) may have both cultural and environmental implications in the near future. Advances in computing through the application of data mining ,and geographic information services provide the tools to conduct •studies that can indicate possible changes in these water catchment areas of WA. The research examines the existing spatial data mining techniques that can be used to interpret trends in WA water catchment land use. Large GIS data sets of the water catchments on Peel-Harvey region have …