azlan Md Zain

An evaluation data of solar irradiation and dry bulb temperature at Subang under Malaysian climate

Solar irradiation and the temperature played a major role in the PV designing system. This factor differs with the latitude, geographic and time of day due to the various sun positions. This paper presents an average of solar irradiation over 10 years data for the Subang area with respect to the complete set data of daily global solar radiation that monitored by the Malaysian Meteorological Department (MMD) from year 1993 to 2002. In this study, the data were analysed to understand the pattern of hourly solar irradiance and also to recommend the appropriate value of daily solar irradiation for the designing of PV system in Malaysia region especially for Subang and Klang Valley. The highest sum of 24 hours average solar irradiation per year was 4.72 kWh m−2 in 1998 as well as temperature 28.5 °C and the annually average solar irradiations for Subang were collected from 3.91 kWh m−2 to 4.30 kWh m−2 during 10 years period. Also, the recommended daily solar irradiation in Malaysian climate is 4.39 kWh m−2, 133.0 kWh m−2 for monthly and the standard value for annual is 1596.5 kWh m−2. The standard deviation for daily solar irradiation is 0.29.

Flood water level modeling and prediction using NARX neural network: Case study at Kelang river

Flood disaster has becomes major threat around the world because it causes loss of lives and damages to property. Thus, reliable flood prediction is very much needed in order to reduce the effects of flood disaster. Hence, an accurate flood water level prediction is an important task to achieve. Since flood water level fluctuation is highly nonlinear, it is very difficult to predict the flood water level. Artificial Neural Network is well known technique is solving nonlinear cases and Nonlinear Auto Regressive with Exogenous Input (NARX) model is one class of Artificial Neural Network model. Thus, this paper proposes flood water level modeling and prediction using Nonlinear Auto Regressive with Exogenous Input (NARX) model to overcome the nonlinearity problem and come out with an advanced neural network model for the prediction of flood water level 10 hours in advance. The input and output parameters used in this model are based on real-time data obtained from Department of Irrigation and Drainage Malaysia. Results showed that NARX model successfully predicted the flood water level 10 hours ahead of time.

Flood water level modelling and prediction using artificial neural network: Case study of Sungai Batu Pahat in Johor

Flood water level prediction has long been the earliest forecasting problems that have attracted the interest of many researchers. Accurate prediction of flood water level is extremely importance as an early warning system to the public to inform them about the possible incoming flood disaster. Using the collected data at the upstream and downstream station of a river, this paper proposes a modelling of flood water level at downstream station using back propagation neural network (BPN). In order to improve the prediction values, an extended Kalman filter was introduced at the output of the BPN. The introduction of extended Kalman filter at the output of BPN shows significant improvement to the prediction and tracking performance of the actual flood water level.

Artificial neural network modelling and flood water level prediction using extended Kalman filter

Accurate flood water level prediction are essential for reliable flood forecasting modelling. Although back propagation neural network (BPN) offer advantages for flood water level prediction, nonlinearity due to input parameters are the major issue to this modelling. A novel Extended Kalman Filter (EKF) optimization algorithm was employed in this study to overcome the nonlinearity problem and come out with an optimal ANN for the prediction of flood water level 3 hours in advance. The inputs used in the algorithm were current values of rainfall at the flood location and three upstream locations of river water levels. The BPN model was trained and tested successfully with Root Mean Square Error (RMSE) and loss function (V) close to zero.

New Artificial Neural Network and Extended Kalman Filter hybrid model of flood prediction system

Accurate prediction of flood water level is a difficult task to achieve due to the nonlinearity of the water level itself and lacking of input parameters to the neural network model. Although Artificial Neural Network is proven to be the best model of flood water level prediction, suitable model parameters need to be chosen for training purposes in order to arrive to an optimal model with smallest error. A new Back Propagation Neural Network model (BPN) for the prediction of flood water level 3 hours ahead of time is developed in this study. This optimized BPN model offers advantages of parameter analysis method instead of trial and error method for choosing the optimized BPN model parameters. However, the simulated results of BPN model required improvement as the model could not able to track the actual water level precisely. Hence, this paper proposes BPN model with integration of EKF at the output. Performance indices result such as Akaikes Final Prediction Error(FPE), Loss Function(V) and Root Mean Square Error (RMSE) from this hybrid model outperform the BPN model result.

7 hours flood prediction modeling using NNARX structure: Case study Kedah

Most of the countries around the world have paid great attention to flood water level prediction system because flood events may damage on peoples life and property. However, since flood water level fluctuates highly nonlinear, it is a very difficult task to predict flood water level accurately. Since Artificial Neural Network is an effective technique for handling nonlinear problems, thus, this paper proposed a 7 hours ahead flood water level prediction modelling using Neural Network Autoregressive with Exogenous Input (NNARX) for flood prone area located in Kedah, Malaysia as case study. The model was developed using four inputs and one output. Three inputs were upstream stations water level and one input from water level differences at downstream flood location. The output was the predicted water level at downstream station. Simulation was done using Matlab Neural Network Toolbox. Results showNNARX modelling was able to predict flood water level ahead of time.

Flood water level prediction and tracking using particle filter algorithm

Most of the countries have paid great attention to flood water level monitoring and tracking because flood may damages peoples life and property. Since flood water level fluctuate highly nonlinear, it is very difficult to predict the flood water level. The particle filter algorithm is well known as a very effective solution for handling nonlinear problems. Thus, in this paper, this algorithm is applied to predict the flood water level. There are many variations of particle filter. This paper proposes Sequential Importance Sampling (SIS) particle filter to solve the above mentioned problem. SIS is the basic particle filter. However, the problems with SIS particle filter are the particle degeneration phenomenon, when after a few iterations only a few particles have nonzero weight. So, Sampling Importance Resampling (SIR) particle filter is also introduced as the improved particle filter. From the simulation results using Matlab, SIR particle filter outperforms SIS particle filter by comparing the Root Mean Square Error (RMSE) value.

Prediction of 4 hours ahead flood water level using improved ENN structure: Case study Kuala Lumpur

Recently, ANN models have been successfully applied in flood water level prediction system. However, most of publication on flood prediction only focusing on flood modelling and no element of prediction time was mentioned. Therefore, flood water level prediction is a new avenue to embark on in order to give early warning for evacuation purposes. This paper proposeda 4 hours ahead flood water level prediction using Improved ENN structure for Kelang River station which is located at Petaling Bridge, Kuala Lumpur. The model was developed using data obtained from the Department of Irrigation and Drainage, Malaysia upon special request. The prediction results of the original Elman neural network structure indicate unsatisfactorily performance results. Therefore, the Improved ENN structure was introduced. The performance indices results concluded that Improved ENN model was more versatile than the original ENN model and significant improvement from the original ENN model can be observed when the Improved ENN was introduced.

Performance analysis of 45.36 kWp grid-connected photovoltaic systems at Malaysia Green Technology Corporation

This paper presents the performance of a 45.36 kWp grid-connected photovoltaic (PV) system at Malaysia Green Technology Corporation (MGTC), Bangi, Malaysia. The site is located at latitude of approximately 2.96°N and longitude of 101.75°E. The system was commissioned on 14th June 2007 and the system performance has been monitored since 1st Feb 2008. The system comprises 45.36 kWp of PV array size, a 40 kW inverter and other balance of system (BOS) components. An irradiance sensor and two temperature sensors are connected externally to the system via the inverter to obtain the irradiance, ambient temperature and module temperature profiles at the site. In contrast, other performance data such as AC current AC voltage, DC current and DC voltage are recorded from the inverter. During the monitoring period, all data were recorded based on 15 minute interval. The system had produced 787.25 MWh of solar electricity since its being monitored with an average daily output of 144 kWh. On the other hand, the inverter efficiency has been fluctuating from 86% to 97%. The average monthly final yield was found to be 96 kWhkWp−1 while the performance ratio (PR) varies from 71.37% to 91.99%.

Review on performance of Thermal Energy Storage system at S & T Complex, UiTM Shah Alam, Selangor

This paper presents on the performance of Thermal Energy Storage (TES) system at Complex Science and Technology, University Teknologi MARA Shah Alam Selangor. Various technical aspects and criteria for thermal energy storage systems and applications are discussed and energy saving techniques and environmental impacts of these systems are highlighted with illustrative examples. This study describes the result of the operation of the TES system. The data obtained were observed and analyzed to measure the cooling load demand capacity, electrical load, and building load profile of S & T Complex in certain duration. The results show that, the operation of TES is not fully utilized. The ice charging process does not constantly meet the nominal tank capacity of 10,800 RTh. Energy consumption is higher since the chiller has to top up the remaining cooling capacity during peak period. Also, in this paper the Building Energy Index of S & T is calculated as 201.48kWh/m2/year. In conclusion, data retrieve will be valuable assets as to forecast and predict the amount of energy use for future benefits.