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Electricity generation is a key contributor to global Greenhouse Gas (GHG) emissions. The urgent need to mitigate climate change demands a transition to more sustainable ways of electricity generation. However, focusing on operational emissions without assessing their life cycle may lead to less effective decisions on energy policy and technology advancements. This study examines the life cycle GHG emissions of major electricity generation systems in Malaysia, employing the life cycle assessment approach. The systems are based on energy resources of coal, natural gas, hydro, and solar photovoltaic (PV). Furthermore, five types of PV systems with different capacity range and module technologies were compared. Furthermore, the study also compares the scenarios of PV installation in major cities in the country. The results show that electricity produced by renewable energy yields substantially lower GHG emissions compared to fossil fuel energy. Throughout their lifetime, PV and hydro electricity systems release GHG emissions at least 5 and 7.4 times lower than coal respectively, and at least 3.5 and 5.2 times lower than natural gas, respectively, under the worst-case uncertainty scenario. Besides, the GHG emissions of PV system installed in major cities in Malaysia ranges from 61.4 to 72.5 g CO2-eq/kWh. The study highlights the potential of renewable energy in promoting sustainability within the energy sector, offering a viable pathway towards the decarbonization of the energy sector. [ABSTRACT FROM AUTHOR]

Electric Energy Consumption (EEC) prediction for building operations can be performed using a Baseline Energy Model (BEM), which is vital to ensure the efficiency of the EEC estimates with its respective independent variables. However, developing the BEM to represent the relationship between independent variables can be a complex task due to the EEC variability in an educational building that differs during its operation period. The best-suited BEM must be continuously improvised to achieve good modeling with accurate and reliable predictions that capture the building operations’ current dynamics. This study aims to conduct a comparative performance assessment between deep learning, machine learning, and statistical models to develop the BEM and, therefore, predict the EEC of the building for 24, 48, 72, and 96 hours, while considering the operation of the lecture weeks and the associated number of students and staff. The hours and temperature are considered as independent variables to be tested with residual error evaluations, whilst the correlation coefficient, coefficient of determination, and training time are also taken into account. Three models with different categories involving Long Short-Term Memory (LSTM), Support Vector Regression (SVR), and AutoRegressive Integrated Moving Average with Exogenous inputs (ARIMAX) were compared, concluding that SVR was the best and can be used as a universal model in the Machine Learning Baseline Energy Model (MLBEM) studies. Accurate EEC prediction will offer a huge advantage for building operators to properly monitor, plan, and manage the EEC, hence avoiding excessive utility bills. [ABSTRACT FROM AUTHOR]

There are various factors that can cause the rise of Neutral-To-Ground Voltage (NTGV) in an educational building. However, based on the pattern of the recorded NTGV, it is difficult to identify the possible factors that contributed to the event. This situation could lead to the wrong decisions to mitigate the corresponding problem. Thus, the main objective of this paper is to verify the high NTGV events based on a model of the educational building's three-phase fourwire system. In this study, the developed educational building electrical network model is integrated with the model for events in a MATLAB/SIMULINK environment. According to the findings, high NTGV events in the building were caused by lightning and ground fault activities. This shows that the model is accurate and suitable to be used for analysing the high NTGV events in an educational building if they are detected. [ABSTRACT FROM AUTHOR]

Wireless energy transfer has been gaining interest due to its potential in wide range of foreseeable applications. As such, this paper explores the use of Neutral Point Type Buck converter to determine the circuit efficiency and capability for a finite range of wireless application. This objective is achieved in two parts; that is through Buck Chopper Converter circuit and Halfwave Rectification Chopper circuit. Magnetic Resonance Coupling is observed as the base principle in performing Wireless Energy Transfer to transmit power with high frequency across large air gap in this study. Results showed that maximum length obtained through the proposed approach has successfully performed an energy transfer of up to 14 centimetres. It was also shown that the hardware construction through the implementation of Neutral Point Type Buck converter was successful in producing desired output waveform and frequency required for wireless energy transfer. [ABSTRACT FROM AUTHOR]