Iskandar Petra

State of the art of smart homes

In this paper we present a review of the state of the art of smart homes. We will first look at the research work related to smart homes from various view points; first in the view point of specific techniques such as smart homes that utilize computer vision based techniques, smart homes that utilize audio-based techniques and then smart homes that utilize multimodal techniques. Then we look at it from the view point of specific applications of smart homes such as eldercare and childcare applications, energy efficiency applications and finally in the research directions of multimedia retrieval for ubiquitous environments. We will summarize the smart homes based research into these two categories. In the survey we found out that some well-known smart home applications like video based security applications has seen the maturity in terms of new research directions while some topics like smart homes for energy efficiency and video summarization are gaining momentum.

Solar PV integration considering grid stability- a case study for the Temburong district in Brunei

In this paper we present a systematic approach to estimate the size of solar PV that can be integrated reliably into a micro-grid. The approach involves three steps, namely, resource analysis, stability studies and finally the optimization of PV size by minimizing the cost of energy (COE). The proposed approach is evaluated on the Temburong micro-grid in Brunei and the evaluation results are presented in this paper.

Financial incentive mechanisms for residential PV systems: An analysis based on the real performance data

One of the major hurdles in popularizing the residential solar projects is its high cost, which has to be borne by individual house owners. Several policy frameworks and incentive mechanisms like Feed in Tariff (FiT) and Net Metering (NM) are being implemented globally to make these projects attractive to the consumers. In this study, we identify the viable FiT and NM for making residential solar PV projects economically attractive in Brunei Darussalam. The analysis is based on the actual performance data collected from a 6kWp Multi-crystalline residential solar PV system. By considering the break-even point of Net Present Value (NPV) of the project, the minimum FiT for the residential PV projects is estimated as

Data driven models for understanding the wind farm wake propagation pattern

0.22 /kWh. Due to the very low electricity tariff in Brunei, the NM rate is highly sensitive to the installed PV size and electricity consumption pattern. Under this condition, the viable NM rate is identified as

Modelling the ramping behaviour of wind turbines

0.74/kWh. Sensitivities of Fit and NM rates on other economic metrics are also presented in the paper.

Intelligent models for the power curves of small wind turbines

In a wind farm, where several wind turbines are arranged in rows and columns, the wind speed available for the downstream turbines are significantly reduced by the wake effect. The wake losses can reduce the total productivity of a wind farm up to 20 per cent. Understanding the wake pattern in an existing wind farm is essential for the short-term wind power forecast. In this paper, we propose the use of artificial intelligence to understand the wind flow pattern and thereby the wake induced power losses within an existing wind farm. The farm considered for this study has 64 wind turbines of 2 MW rated capacity. Three learning methods based on artificial intelligence are used for the study. These are (i) Artificial Neural Network (ANN), (ii) Support Vector Machines (SVM), and (iii) K-Nearest Neighbors (KNN). The accuracies of these models, based on the error between the estimated and observed power produced by the turbines, are also presented.

Prescheduled Controller for Split Unit Air Conditioner Reduce Energy Consumption

Being stochastic phenomena, magnitude and direction of wind spectra, available at a site, may vary with time which results in frequent changes in the output from the wind turbines. This frequent fluctuation in the output from the turbines makes the grid integration of wind energy systems rather challenging. Understanding the ramping behaviour of wind turbines under fluctuating wind conditions is essential for the efficient management of the power grids integrated with different generating options. In this paper, a mathematical model is presented to estimate the ramping behaviour of wind turbines by considering the Weibull probability distribution of wind velocity and the power curve analytics of the wind turbine. The developed model was tested with the real performance data from a wind turbine of 2MW rated capacity. By analyzing the Normalized Root Mean Squared Error between the estimated and observed performances, it was found that the model could predict the ramp events with an accuracy of around 85 per cent.

Inverse Kinematic Solutions Using Artificial Neural Networks

Along with the rapid expansion of the MW sized big wind turbine sector, the small wind turbine industry is also growing. Understanding the power response of these systems to the variations in wind velocity is essential for the optimal selection and efficient management of these turbines. This is defined by the power curves of wind turbines. In this paper, we propose nonparametric models for the power curves of two small wind turbines of 50 kW and 2.5 kW rated capacities, based on the manufacturer power curve. Four different machine learning methods viz. Artificial Neural Network (ANN), Support Vector Machines (SVM), k-Nearest Neighbors (KNN) and Gradient Boosting Machines (GBM) were used for the modeling. The accuracies of these models are validated by estimating the error between the model output and the field observations from these turbines. With the lowest NRMSE values of 0.16 and 0.12, ANN-based models are found to be more reliable in defining the velocity-power performances of the turbines.

Synthesis and Characterization of High Density and Low Temperature Sintered Proton Conductor BaCe0.5Zr0.35In0.1Zn0.05O3-δ

Air conditioners are one of the highest power consuming devices in houses or industrial buildings. Most buildings require air conditioner to make offices, classrooms, public areas or rooms to meet the required thermal comfort. Split unit type air conditioners are widely used in Negara Brunei Darussalam and other ASEAN countries. Currently, prescheduled controllers for split-unit air conditioners are not available in the market. In order to reduce air conditioner power consumption, study have been done to measured energy saving by controlling air conditioning using prescheduled controller that designed which is suitable for fixed operation hours example offices, schools, shops and others. Result showed that average percentage of power saving using a prescheduled controller can be as high as 40%.

Case study on the feasibility of renewable integration in the Temburong Island of Brunei

Inverse Kinematics solutions are needed for control of robotic manipulators for successful task execution. It is the process of obtaining the required manipulator joint angle values for a given desired end point position and orientation. In general the process of obtaining these joint angle values is a complex process that may require some higher computational power in the hardware. Mainly there are three traditional methods used to solve inverse kinematics problem, namely; geometric methods, algebraic methods and iterative methods. Apart from these traditional techniques researchers have looked into the use of Artificial Neural Networks (ANNs). In this paper we re-visit these non-traditional techniques and compare the advantages and disadvantages of each method.