Garuda - Garba Rujukan Digital

Killer whale-backpropagation (KW-BP) algorithm for accuracy improvement of neural network forecasting models on energyefficient data Saadi Bin Ahmad Kamaruddin; Nor Azura Md Ghani; Hazrita Ab Rahim; Ismail Musirin
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 3: September 2019
Publisher : Institute of Advanced Engineering and Science

Green technology building is not newly introduced to the world nor Malaysia, but it is rarely practiced globally and now it has promoted noteworthy due to destructions caused by human hands towards the nature. Now people started to realize that the world is polluted by many hazardous substances. Therefore, Help University came up with the effort of preserving the nature through a new Green Technology campus, which has been fully operated since year 2017. In this research, neural network forecasting models on energy-efficient data of Help University, Subang 2 green technology campus at Subang Bistari, Selangor has been done with respect to value-formoney (VFM) attribute. Previously there were no similar research done on energy-efficient data of Help University, Subang 2 campus. The significant factors with respect to energy or electricity saved (MW/hr) in the year 2017 variable were studied as recorded by Building Automation and Control System (BAS) of Help University Subang 2 campus. Using multiple linear regression (stepwise method), the significant predictor towards energy saved (MW/hr) was Building Energy Index (BEI) (kWh/m2/year) based p-value<α=0.05. A mathematical model was developed. Moreover, the proposed neural network forecasting model using Killer WhaleBackpropagation Algorithm (KWBP) were found to better than existing conventional techniques to forecast BEI data. This research is expected to specifically assist maintenance department of Help University, Subang 2 campus towards load forecasting for power saving planning in years to come.

Factors contributing towards research productivity in higher education Caroline Henry; Nor Azura Md Ghani; Umi Marshida Abd Hamid; Ahmad Naqiyuddin Bakar
International Journal of Evaluation and Research in Education (IJERE) Vol 9, No 1: March 2020
Publisher : Institute of Advanced Engineering and Science

Research Productivity (RP) is the key element in the establishment of ranking and rating system in the Higher Education (HE) sector. Despite of the many initiatives taken to enliven the research culture among academic staff, there are still constraints and resistance towards conducting research. Therefore, this study attempts to identify the factors affecting RP and develop an appropriate model to determine the RP of an academic staff in Universiti Teknologi MARA (UiTM). In this study, 5 research related indicators were used in the determination of RP. Since the population size of UiTM is large, the primary data was collected by using questionnaire survey and stratified random sampling. The variables that were found to be significant in determining RP of an academic staff were age cohort, highest qualification, cluster and track emphasis. Satisfaction towards annual KPI, UiTM current policy and monthly income were also found to influence the RP of an academic staff. In addition, perceiving the role of principal investigator as a chore and burden and supervising and graduating a PhD student perception as burden and pleasure were also found to be affecting RP. Using these variables, Logistic Regression Model was used to determine the RP of an academic staff in UiTM. In conclusion, personal, environmental and behavioural factors were found to have influence on the RP among academic staff of UiTM. Therefore, generally it is possible to maximize the RP of academic staff by identifying the factors influencing RP followed by strategic management and proper monitoring system.

Load Management for Voltage Control Study Using Parallel Immunized-computational Intelligence Technique Amirul Izzat Abu Bakar; Mohamad Khairuzzaman Mohamad Zamani; Ismail Musirin; Nor Azura Md Ghani
Bulletin of Electrical Engineering and Informatics Vol 7, No 2: June 2018
Publisher : Institute of Advanced Engineering and Science

The increase of power demand is a crucial issue in the power system community in many parts of the world. Malaysia has also witnessed the familiar scenario due to the current development throughout the country has invited the urgency of increase in the power supply. Since Malaysia practices vertical system; where the electricity is supplied by only one utility, load management is an important issue so that the delivery of electricity is implemented without discrimination. Parallel Computational Intelligence will be developed which can alleviate and avoid all the unsolved issues, highlighting the weakness of current schemes. Parallel Computational Intelligence is developed to manage the optimal load in making sure the system maintains the stability condition, within the voltage limits. This paper presents evolutionary programming (EP) technique for optimizing the voltage profile. In this study, 3 algorithms which are Gaussian, Cauchy and Parallel EP were developed to solve optimal load management problem on IEEE 26-bus Reliability Test System (RTS). Results obtained from the study revealed that the application of Parallel EP has significantly reduced the time for the optimization process to complete.

FACTS device installation in transmission system using whale optimization algorithm Irham Syafiq Shahbudin; Ismail Musirin; Saiful Izwan Suliman; Afdallyna Fathiyah Harun; Sharifah Azma Syed Mustaffa; Hadi Suyono; Nor Azura Md Ghani
Bulletin of Electrical Engineering and Informatics Vol 8, No 1: March 2019
Publisher : Institute of Advanced Engineering and Science

As the world is progressing forward, the load demand in the power system has been continuously increasing day by day. This situation has forced the power system to operate under stress condition due to its limitation. Therefore, due to the stressed condition, the transmission losses faced higher increment with a lower minimum voltage. Theoretically, the installation of the Flexible AC Transmission System (FACTS) device can solve the problem experienced by the power system. This paper presents the whale optimization algorithm for loss minimization using FACTS devices in the transmission system. Thyristor controlled series compensator (TCSC) is chosen for this study. In this study, WOA is developed to identify the optimal sizing of FACTS device for loss minimization in the power system. IEEE 30- bus RTS was used as the test system to validate the effectiveness of the proposed algorithm.

Results of Fitted Neural Network Models on Malaysian Aggregate Dataset Nor Azura Md Ghani; Saadi Bin Ahmad Kamaruddin; Ismail Musirin; Hishamuddin Hashim
Bulletin of Electrical Engineering and Informatics Vol 7, No 2: June 2018
Publisher : Institute of Advanced Engineering and Science

This result-based paper presents the best results of both fitted BPNN-NAR and BPNN-NARMA on MCCI Aggregate dataset with respect to different error measures. This section discusses on the results in terms of the performance of the fitted forecasting models by each set of input lags and error lags used, the performance of the fitted forecasting models by the different hidden nodes used, the performance of the fitted forecasting models when combining both inputs and hidden nodes, the consistency of error measures used for the fitted forecasting models, as well as the overall best fitted forecasting models for Malaysian aggregate cost indices dataset.

Load Management for Voltage Control Study Using Parallel Immunized-computational Intelligence Technique Amirul Izzat Abu Bakar; Mohamad Khairuzzaman Mohamad Zamani; Ismail Musirin; Nor Azura Md Ghani
Bulletin of Electrical Engineering and Informatics Vol 7, No 2: June 2018
Publisher : Institute of Advanced Engineering and Science

The increase of power demand is a crucial issue in the power system community in many parts of the world. Malaysia has also witnessed the familiar scenario due to the current development throughout the country has invited the urgency of increase in the power supply. Since Malaysia practices vertical system; where the electricity is supplied by only one utility, load management is an important issue so that the delivery of electricity is implemented without discrimination. Parallel Computational Intelligence will be developed which can alleviate and avoid all the unsolved issues, highlighting the weakness of current schemes. Parallel Computational Intelligence is developed to manage the optimal load in making sure the system maintains the stability condition, within the voltage limits. This paper presents evolutionary programming (EP) technique for optimizing the voltage profile. In this study, 3 algorithms which are Gaussian, Cauchy and Parallel EP were developed to solve optimal load management problem on IEEE 26-bus Reliability Test System (RTS). Results obtained from the study revealed that the application of Parallel EP has significantly reduced the time for the optimization process to complete.

Results of Fitted Neural Network Models on Malaysian Aggregate Dataset Nor Azura Md Ghani; Saadi Bin Ahmad Kamaruddin; Ismail Musirin; Hishamuddin Hashim
Bulletin of Electrical Engineering and Informatics Vol 7, No 2: June 2018
Publisher : Institute of Advanced Engineering and Science

This result-based paper presents the best results of both fitted BPNN-NAR and BPNN-NARMA on MCCI Aggregate dataset with respect to different error measures. This section discusses on the results in terms of the performance of the fitted forecasting models by each set of input lags and error lags used, the performance of the fitted forecasting models by the different hidden nodes used, the performance of the fitted forecasting models when combining both inputs and hidden nodes, the consistency of error measures used for the fitted forecasting models, as well as the overall best fitted forecasting models for Malaysian aggregate cost indices dataset.

FACTS device installation in transmission system using whale optimization algorithm Irham Syafiq Shahbudin; Ismail Musirin; Saiful Izwan Suliman; Afdallyna Fathiyah Harun; Sharifah Azma Syed Mustaffa; Hadi Suyono; Nor Azura Md Ghani
Bulletin of Electrical Engineering and Informatics Vol 8, No 1: March 2019
Publisher : Institute of Advanced Engineering and Science

As the world is progressing forward, the load demand in the power system has been continuously increasing day by day. This situation has forced the power system to operate under stress condition due to its limitation. Therefore, due to the stressed condition, the transmission losses faced higher increment with a lower minimum voltage. Theoretically, the installation of the Flexible AC Transmission System (FACTS) device can solve the problem experienced by the power system. This paper presents the whale optimization algorithm for loss minimization using FACTS devices in the transmission system. Thyristor controlled series compensator (TCSC) is chosen for this study. In this study, WOA is developed to identify the optimal sizing of FACTS device for loss minimization in the power system. IEEE 30- bus RTS was used as the test system to validate the effectiveness of the proposed algorithm.

Load Management for Voltage Control Study Using Parallel Immunized-computational Intelligence Technique Amirul Izzat Abu Bakar; Mohamad Khairuzzaman Mohamad Zamani; Ismail Musirin; Nor Azura Md Ghani
Bulletin of Electrical Engineering and Informatics Vol 7, No 2: June 2018
Publisher : Institute of Advanced Engineering and Science

The increase of power demand is a crucial issue in the power system community in many parts of the world. Malaysia has also witnessed the familiar scenario due to the current development throughout the country has invited the urgency of increase in the power supply. Since Malaysia practices vertical system; where the electricity is supplied by only one utility, load management is an important issue so that the delivery of electricity is implemented without discrimination. Parallel Computational Intelligence will be developed which can alleviate and avoid all the unsolved issues, highlighting the weakness of current schemes. Parallel Computational Intelligence is developed to manage the optimal load in making sure the system maintains the stability condition, within the voltage limits. This paper presents evolutionary programming (EP) technique for optimizing the voltage profile. In this study, 3 algorithms which are Gaussian, Cauchy and Parallel EP were developed to solve optimal load management problem on IEEE 26-bus Reliability Test System (RTS). Results obtained from the study revealed that the application of Parallel EP has significantly reduced the time for the optimization process to complete.

Results of Fitted Neural Network Models on Malaysian Aggregate Dataset Nor Azura Md Ghani; Saadi Bin Ahmad Kamaruddin; Ismail Musirin; Hishamuddin Hashim
Bulletin of Electrical Engineering and Informatics Vol 7, No 2: June 2018
Publisher : Institute of Advanced Engineering and Science

This result-based paper presents the best results of both fitted BPNN-NAR and BPNN-NARMA on MCCI Aggregate dataset with respect to different error measures. This section discusses on the results in terms of the performance of the fitted forecasting models by each set of input lags and error lags used, the performance of the fitted forecasting models by the different hidden nodes used, the performance of the fitted forecasting models when combining both inputs and hidden nodes, the consistency of error measures used for the fitted forecasting models, as well as the overall best fitted forecasting models for Malaysian aggregate cost indices dataset.

Title

Found 12 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 12 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 12 Documents
Search