This page has only limited features, please log in for full access.
Expeditious urbanization and rapid industrialization have significantly influenced the rise of energy demand globally in the past two decades. Solar energy is considered a vital energy source that addresses this demand in a cost-effective and environmentally friendly manner. Improving solar cell efficiency is considered a prerequisite to reinforcing silicon solar cells’ growth in the energy market. In this study, the influence of various parameters like the thickness of the absorber or wafer, doping concentration, bulk resistivity, lifetime, and doping levels of the emitter and back surface field, along with the surface recombination velocity (front and back) on solar cell efficiency was investigated using PC1D simulation software. Inferences from the results indicated that the bulk resistivity of 1
Gokul Sidarth Thirunavukkarasu; Mehdi Seyedmahmoudian; Jaideep Chandran; Alex Stojcevski; Maruthamuthu Subramanian; Raj Marnadu; S. Alfaify; Mohd. Shkir. Optimization of Mono-Crystalline Silicon Solar Cell Devices Using PC1D Simulation. Energies 2021, 14, 4986 .
AMA StyleGokul Sidarth Thirunavukkarasu, Mehdi Seyedmahmoudian, Jaideep Chandran, Alex Stojcevski, Maruthamuthu Subramanian, Raj Marnadu, S. Alfaify, Mohd. Shkir. Optimization of Mono-Crystalline Silicon Solar Cell Devices Using PC1D Simulation. Energies. 2021; 14 (16):4986.
Chicago/Turabian StyleGokul Sidarth Thirunavukkarasu; Mehdi Seyedmahmoudian; Jaideep Chandran; Alex Stojcevski; Maruthamuthu Subramanian; Raj Marnadu; S. Alfaify; Mohd. Shkir. 2021. "Optimization of Mono-Crystalline Silicon Solar Cell Devices Using PC1D Simulation." Energies 14, no. 16: 4986.
In this paper, a novel deep neural network-based energy prediction algorithm for accurately forecasting the day-ahead hourly energy consumption profile of a residential building considering occupancy rate is proposed. Accurate estimation of residential load profiles helps energy providers and utility companies develop an optimal generation schedule to address the demand. Initially, a comprehensive multi-criteria analysis of different machine learning approaches used in energy consumption predictions was carried out. Later, a predictive micro-grid model was formulated to synthetically generate the stochastic load profiles considering occupancy rate as the critical input. Finally, the synthetically generated data were used to train the proposed eight-layer deep neural network-based model and evaluated using root mean square error and coefficient of determination as metrics. Observations from the results indicated that the proposed energy prediction algorithm yielded a coefficient of determination of 97.5% and a significantly low root mean square error of 111 Watts, thereby outperforming the other baseline approaches, such as extreme gradient boost, multiple linear regression, and simple/shallow artificial neural network.
Le Truong; Ka Chow; Rungsimun Luevisadpaibul; Gokul Thirunavukkarasu; Mehdi Seyedmahmoudian; Ben Horan; Saad Mekhilef; Alex Stojcevski. Accurate Prediction of Hourly Energy Consumption in a Residential Building Based on the Occupancy Rate Using Machine Learning Approaches. Applied Sciences 2021, 11, 2229 .
AMA StyleLe Truong, Ka Chow, Rungsimun Luevisadpaibul, Gokul Thirunavukkarasu, Mehdi Seyedmahmoudian, Ben Horan, Saad Mekhilef, Alex Stojcevski. Accurate Prediction of Hourly Energy Consumption in a Residential Building Based on the Occupancy Rate Using Machine Learning Approaches. Applied Sciences. 2021; 11 (5):2229.
Chicago/Turabian StyleLe Truong; Ka Chow; Rungsimun Luevisadpaibul; Gokul Thirunavukkarasu; Mehdi Seyedmahmoudian; Ben Horan; Saad Mekhilef; Alex Stojcevski. 2021. "Accurate Prediction of Hourly Energy Consumption in a Residential Building Based on the Occupancy Rate Using Machine Learning Approaches." Applied Sciences 11, no. 5: 2229.
The solar photo-voltaic systems control architecture has a substantial influence over the cost, efficiency, and accuracy of maximum power point tracking under partial shading conditions. In this paper, a novel distributed architecture of a building integrated photo-voltaic system equipped with a single maximum power point tracking controller is presented in order to address the drawbacks associated with respect to cost, complexity and efficiency of the existing photo-voltaic system architectures. In addition, a radial movement optimization based maximum power point tracking control algorithm is designed, developed, and validated using the proposed system architecture under five different partial shading conditions. The inferences obtained from the validation results of the proposed distributed system architecture indicated that cost was reduced by 75% when compared to the commonly used decentralised systems. The proposed distributed building integrated photo-voltaic system architecture is also more efficient, robust, reliable, and accurate.
Mehdi Seyedmahmoudian; Gokul Thirunavukkarasu; Elmira Jamei; Tey Soon; Ben Horan; Saad Mekhilef; Alex Stojcevski. A Sustainable Distributed Building Integrated Photo-Voltaic System Architecture with a Single Radial Movement Optimization Based MPPT Controller. Sustainability 2020, 12, 6687 .
AMA StyleMehdi Seyedmahmoudian, Gokul Thirunavukkarasu, Elmira Jamei, Tey Soon, Ben Horan, Saad Mekhilef, Alex Stojcevski. A Sustainable Distributed Building Integrated Photo-Voltaic System Architecture with a Single Radial Movement Optimization Based MPPT Controller. Sustainability. 2020; 12 (16):6687.
Chicago/Turabian StyleMehdi Seyedmahmoudian; Gokul Thirunavukkarasu; Elmira Jamei; Tey Soon; Ben Horan; Saad Mekhilef; Alex Stojcevski. 2020. "A Sustainable Distributed Building Integrated Photo-Voltaic System Architecture with a Single Radial Movement Optimization Based MPPT Controller." Sustainability 12, no. 16: 6687.
Reducing the environmental impacts caused by conventional power sources in smart grids, achieving socio-economic sustainability, and effectively addressing the rapidly increasing energy demand are some of the critical characteristics of demand-side management systems. In this paper, a multi-agent-based decentralised relaxed-constrained energy management strategy for a community-based residential microgrid system using demand-side management is presented. The proposed demand-side management system controls the creative decision-making process of the residential customer agents interconnected within the proposed residential microgrid system. The main objectives of the proposed demand-side management controllers are to make decisions that reduce the peak demand of the load to each agent and to reshape the profile of the power load based on their energy consumption pattern. In addition to this, the novel realistic appliance models with discrete operational levels and on–off capabilities proposed in this research makes the optimisation process a non-convex mixed-integer problem. The proposed decentralised optimisation scheme addressed this issue, by initially relaxing the constraints on the appliances and then using the gradient descent algorithm to decompose and solve the realistic schedules for the devices in the scheduling period. Results indicated that the proposed decentralised relaxed constrain approach is more feasible, effective, economical and efficient in addressing the energy management problem of a residential community microgrid.
Roozbeh Morsali; Gokul Sidarth Thirunavukkarasu; Mehdi Seyedmahmoudian; Alex Stojcevski; Ryszard Kowalczyk. A relaxed constrained decentralised demand side management system of a community-based residential microgrid with realistic appliance models. Applied Energy 2020, 277, 115626 .
AMA StyleRoozbeh Morsali, Gokul Sidarth Thirunavukkarasu, Mehdi Seyedmahmoudian, Alex Stojcevski, Ryszard Kowalczyk. A relaxed constrained decentralised demand side management system of a community-based residential microgrid with realistic appliance models. Applied Energy. 2020; 277 ():115626.
Chicago/Turabian StyleRoozbeh Morsali; Gokul Sidarth Thirunavukkarasu; Mehdi Seyedmahmoudian; Alex Stojcevski; Ryszard Kowalczyk. 2020. "A relaxed constrained decentralised demand side management system of a community-based residential microgrid with realistic appliance models." Applied Energy 277, no. : 115626.
The rapid advancement of technology, including the internet of things (IoT), industry 4.0, and smart cities, revealed an excess need for career-ready graduates. It is expected that a career-ready graduate is technically competent and possess professional skills acquired via the experiential learning incorporated into the curriculum. But the gap exists with the learners understanding of requirements and opportunities associated with graduate employability. In this research, we focus on evaluating the learners’ experiences, expectations, and perceptions of graduate employability in an engineering curriculum. In this research, the interpretations of students on the graduate employability and the extent of influence that exists based on the learning outcomes of the graduate course are examined. The gaps between the academic environment and graduate employability awareness are highlighted. Later, a national language processing-based sentiment analyzer is used to evaluate the student’s perceptions. Results from the analysis portrayed that the different levels of expectation and experiences that prevailed in the graduate course based on the conceptual idea of graduate employability need substantial focus in future curriculum development.
Gokul Thirunavukarasu; Siva Chandrasekaran; Varsha Subhash Betageri; John Long. Assessing Learners’ Perceptions of Graduate Employability. Sustainability 2020, 12, 460 .
AMA StyleGokul Thirunavukarasu, Siva Chandrasekaran, Varsha Subhash Betageri, John Long. Assessing Learners’ Perceptions of Graduate Employability. Sustainability. 2020; 12 (2):460.
Chicago/Turabian StyleGokul Thirunavukarasu; Siva Chandrasekaran; Varsha Subhash Betageri; John Long. 2020. "Assessing Learners’ Perceptions of Graduate Employability." Sustainability 12, no. 2: 460.
William VanDeventer; Elmira Jamei; Gokul Sidarth Thirunavukkarasu; Mehdi Seyedmahmoudian; Tey Kok Soon; Ben Horan; Saad Mekhilef; Alex Stojcevski. Short-term PV power forecasting using hybrid GASVM technique. Renewable Energy 2019, 140, 367 -379.
AMA StyleWilliam VanDeventer, Elmira Jamei, Gokul Sidarth Thirunavukkarasu, Mehdi Seyedmahmoudian, Tey Kok Soon, Ben Horan, Saad Mekhilef, Alex Stojcevski. Short-term PV power forecasting using hybrid GASVM technique. Renewable Energy. 2019; 140 ():367-379.
Chicago/Turabian StyleWilliam VanDeventer; Elmira Jamei; Gokul Sidarth Thirunavukkarasu; Mehdi Seyedmahmoudian; Tey Kok Soon; Ben Horan; Saad Mekhilef; Alex Stojcevski. 2019. "Short-term PV power forecasting using hybrid GASVM technique." Renewable Energy 140, no. : 367-379.
The time taken for the scheduling task in a control system to reduce the traffic within the system is one of significant field of research in modern era. There are different control systems that require time scheduling such as elevator control system, traffic control system and train control system. Currently, there are unique control logic strategies adopting scheduling algorithm that are implemented in real time systems like earliest deadline first and ant colony optimization. At the same time, the disadvantages possessed by them are the exponential dip in the performance ratio due to over loading. Despite of all the available resources there are many issues faced such as congestion in traffic networks due to non-adaptive scheduling algorithms, etc., which led to several misfortunes and danger for human life. Hence an improved algorithm that increases the efficiency of the system is required to validate the processing time and the deadlines. Our research is focused on validating a proposed idea of using Arduino microcontroller to implement the different scheduling tasks and validate the efficiency of the algorithm to optimize the results of the system. This take cares of assigning the critical paths which priorities the tasks and focuses on reducing the scheduling time. This rapidly increases the processing speed and efficiency of the algorithm. We plan to use the Arduino board which has an inbuilt error detection algorithm that helps in checking whether the time scheduling is done effectively. In the initial phase of the project we develop and fabricate the hardware design using CAD design software packages like Solid Works. This is later employed with suitable environmental interfaces like, sensors and microcontrollers that can work in an adaptable environment as per requirements to validate the scheduling algorithm. The scheduling algorithm can also be used for controlling the current flow and power storage which will contribute a lot in the power consumption aspect. Graphical data interpretation of various algorithms from the past literature is observed and few selected ones are to be implemented in the experimental set up that is built as an initial proof of concept. By analyzing the results from the simulations carried out using the Altera FPGA board with VHDL and Arduino it is clear that we obtain better results using the Arduino board. Finally, to have an extensive study on different intelligent control logics that are used in the above mentioned control systems, we use the prototyped miniature model of an elevator system and a train control system to validate the different disk scheduling approaches like First Come-First Serve (FCFS), Elevator (SCAN) and ant colonization to solve the discrete combinational optimization of the scheduling logic. Initial validation of the system focuses on the effectiveness of using the ant colonization strategies to enhances the efficiency of the scheduling algorithm and optimize it for real time application.
Gokul Sidarth Thirunavukkarasu; Ragil Krishna. Scheduling Algorithm for Real-Time Embedded Control Systems using Arduino Board. KnE Engineering 2017, 2, 258 .
AMA StyleGokul Sidarth Thirunavukkarasu, Ragil Krishna. Scheduling Algorithm for Real-Time Embedded Control Systems using Arduino Board. KnE Engineering. 2017; 2 (2):258.
Chicago/Turabian StyleGokul Sidarth Thirunavukkarasu; Ragil Krishna. 2017. "Scheduling Algorithm for Real-Time Embedded Control Systems using Arduino Board." KnE Engineering 2, no. 2: 258.