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The geographical location (latitude: 24° 16′ N and longitude: 55° 36′ E) of Al Ain city in the southwest of United Arab Emirates (UAE) favors the development and utilization of solar energy. This paper presents an artificial neural network (ANN) approach for predicting monthly global solar radiation (MGSR) on a horizontal surface in Al Ain. The ANN models are presented and implemented on 13-year measured meteorological data for Al Ain such as maximum temperature, mean wind speed, sunshine, and mean relative humidity between 1995 and 2007. The meteorological data between 1995 and 2004 are used for training the ANN and data between 2004 and 2007 are used for testing the predicted values. Multilayer perceptron (MLP) and radial basis function (RBF) neural networks are used for the modeling. Models for the MGSR were obtained using eleven combinations of data sets based on the above mentioned measured data for Al Ain city. Forecasting performance parameters such as root mean square error (RMSE), mean bias error...

This paper presents empirical regression models for predicting the daily and monthly average global solar radiation in the United Arab Emirates (UAE) based on 9-year measured data. It also computes the diffuse solar radiation based on well- established models. The UAE enjoys plenty of sunny days with an annual average of 9.6 daily sunshine hours. This attests to the abundance of solar and its potential to produce clean energy sources.

This paper presents a non-linear regression model based on an evolutionary computing technique namely the genetic programming for estimating solar radiation. This approach aims to estimate the best formula that represents the function for estimating the global solar radiation on horizontals with respect to the measured climatological data. First, we present a reference approach to find one global formula that models the relation among the solar radiation amount and a set of weather factors. In the second step, we present an enhanced approach that consists of multi formulas of regression in a parallel structure. The performance of the proposed approaches has been evaluated using statistical analysis measures. The obtained results were promising and comparable to those obtained by other empirical and neural models conducted by other research groups.

Electricity in Lebanon is facing many problems leading to serious implications on the economic, social, and industrial levels of this country. The big deficit in electricity generation and not meeting the demand is by far the most important problem that needs an immediate. This paper suggests a new idea to reinforce the capacity of existing thermal power plants in Lebanon with the use of photovoltaic (PV) panels. Two thermal power plants will be investigated as case studies; Zouk and Jiyeh power plants. The added PV elements will produce additional energy at a lower cost compared to traditional PV systems. The low cost of the additional PV system in the thermal power plant is obtained through the use of low cost lands and avoiding the use of batteries for energy storage.

The Lebanese electricity system system's deteriorating situation (mainly characterized by the daily long rationing hours), has led to the wide use of diesel generators by Lebanese citizens to compensate for the outage hours. Unfortunately, this has led to lower energy efficiency, higher cost of power production, and notably higher greenhouse gas emissions. In this paper, a renewable-based micro-grid system is proposed, which provides the needed backup power at a lower cost and carbon footprint. In order to increase the flexibility of the system and decrease the payback time, the concept of sharing between houses is applied. Moreover, the extra generated power is injected into the national grid through the net-metering protocol. This feasibility study is applied to 3 types of houses in Lebanon (small, medium, and large). This system could pave the way towards the wide-scale implementation of micro grids and smart grids in Lebanon. In general, it has 3 times less payback period and 5–23 tons of annual CO2 reduction per house.

A tool, “MY PV TOOL”, has been developed in this work to study the behavior of series and shunt resistances in PV modules. Experimental as well as theoretical equations have been used to determine the series and shunt resistances for a given commercial PV module.

It is well-known that the bottleneck of Photovoltaic (PV) systems is the use of storage batteries and the area needed to install the system. The main idea in this work is to design a large PV system with almost zero cost of land needed for the installation, and without the need of batteries. The proposed PV system makes use of special areas of the Lebanese lands; the middle of high-ways which are usually left unoccupied without being exploited by any other business. This way, the traditional high cost of PV systems residing in the land and storage is eliminated. The fact that we do not need batteries in the proposed PV system results from the ability to connect the output power directly to the public utility grid. The design consists of 20,476 PV panels that cover the middle of the high-way extending from Tyre to Saida, with a distance of 27,300 meters and 2 meters wide. The resulting PV system has a capacity of 6.2 MW, and the total initial cost is ∼8.845 M$, however, after 25 years (which is the proposed lifetime of the PV system) the project would generate a total of ∼54.7 M$. Moreover, the PV system would provide net reduction of CO2 and GHG emissions of ∼7,388 tCO2/year.

Mathematical expressions have been employed to estimate global solar radiation on horizontal from relative sunshine duration for two weather stations in the United Arab Emirates (UAE), which are Abu Dhabi and Al Ain. These expressions include the original Angstrom-Prescott regression function (linear), quadratic function, third-order function, single-term exponential function, power function, logarithmic, and linear-logarithmic function. The predicted values were compared to the measured values using number of statistical methods to validate the goodness of the fits, such as residual analysis and goodness of fit statistics. All the used mathematical models performed generally well in both cities of Abu Dhabi and Al Ain, with all values of the coefficient of determination (R2) higher than 75%. Specifically, the linear Angstrom-Prescott model estimated the average monthly global radiation on horizontal best for the city of Abu Dhabi, providing the second lowest mean absolute percentage error (MAPE) of 1.89% and the highest value of R2, which is approximately 94%, while the third-order model proved to be the best estimator for the city of Al Ain, providing the lowest MAPE value (3.06%) and a corresponding R2 of 83%.

This paper discusses the complexity of reduced ordered binary decision diagrams (ROBDDs) for Boolean functions with XOR/XNOR min-terms. Knowing the number of variables and the number of product terms of Boolean function containing only XOR/XNOR min-terms, one can predict the number of nodes in its ROBDD representation without building the binary decision diagram (BDD). A mathematical model for this prediction has been developed. This model can be used to find the maximum number of nodes for a given number of variables. Theoretical and experimental results are reported to underline the efficiency of this approach. The experimental results show that even though the XOR/XNOR min-terms cannot be simplified using Boolean laws or any other simplification method leading to a better min-term representation, the ROBDD will perform the simplification using the ROBDD reduction rules. The required memory is analysed for different methods of representation, and this analysis showed that ROBDDs are memory efficient str...

We propose in this paper a programmable band-pass filter based on an array of fully differential transconductance circuits that controls the filter parameters. The signal path does not contain any switch, and fine-tuning of the filter parameters is implemented using programmable capacitors. A digital building block is implemented in the proposed band-pass filter to tune its central frequency as well as its bandwidth. The filter circuit is based on a biquad topology, which is designed and implemented with CMOS 0.18 μm technology. Experimental results show a programming ability of the center frequency between 5.9 and 58 MHz, and the quality factor can be tuned from 0.36 to 10. These features are obtained for a total power consumption of less than 10.5 mW from a single 1.8 V power supply.

This paper describes a neural network approach that gives an estimation method for the space complexity of Binary Decision Diagrams (BDDs). A model has been developed to predict the complexity of digital circuits. The formal core of the developed neural network model (NNM) is a unique matrix for the complexity estimation over a set of BDDs derived from Boolean logic expressions with a given number of variables and Sum of Products (SOP) terms. Experimental results show good correlation between the theoretical results and those predicted by the NNM, which will give insights to the complexity of Very Large Scale Integration (VLSI)/Computer Aided Design (CAD) designs. The proposed model is capable of predicting the maximum BDD complexity (MaxBC) and the number of product terms (NPT) in the Boolean function that corresponds to the minimum BDD complexity (MinBC). This model provides an alternative way to predict the complexity of digital VLSI circuits.

This chapter presents empirical regression models for estimating the monthly average daily global horizontal irradiance in the capital city of Abu Dhabi, United Arab Emirates (UAE), based on the measurement data of 9 years. It also addresses selected models available in the literature for the estimation of diffuse horizontal irradiance and their correlation to measured data. The effect of atmospheric turbidity, due to humidity, sandstorms, and aerosols, on the solar irradiance is also addressed.

This study suggests a simple and robust control strategy for improving captured power in heaving wave energy converters (WEC). The controller aims at optimizing the captured power using a hierarchical control strategy (HCS), which consists of a higher and lower control levels. The top level control is responsible of producing the reference velocity that is used by the lower level controller to regulate the actual velocity of the WEC buoy. The reference is generated by solving an offline optimization problem for maximizing the absorbed energy subject to the buoy motion constraints. As an attempt to avoid computational complexity in real-time operation, a simple look-up table is produced for various sea-states conditions. In order to track this reference, sliding mode controller (SMC) is deployed. The design parameters of the SMC is tuned offline using a well-known model of WEC. Any perturbations, nonlinearities, and disturbances not covered by the used model will be handled by the robustness property of the SMC. Moreover, the used SMC does not add any further complexity to the overall control strategy. The proposed method is validated in both regular and irregular sea-states, as well as in both nominal and perturbed scenarios. Other control strategies were also used to further assess the performance of the proposed control strategy. Based on the simulation results, the proposed hierarchical controller outperformed the other controllers in both nominal and perturbed scenarios.

Information about the distribution of path-lengths in a Binary Decision Diagrams (BDDs) representing Boolean functions is useful in determining the speed of hardware and software implementations of the circuit represented by these Boolean functions. This study presents expressions produced from an empirical analysis of a representative collection of Boolean functions. The Average Path Length (APL) and the Shortest Path Length (SPL) have simple behavior as function of the number of variables and the number of terms used in the construction of the Sum of Products (SOPs) in Boolean expressions. We present a generic expression that is uniformly adaptable to each curve of path-length versus number of terms over all the empirical data. This expression makes it possible to estimate the performance characteristics of a circuit without building its BDD. This approach applies to any number of variables, number of terms, or variable ordering method.

We propose in this paper a tunable second order band-pass filter based on two CMOS current feedback operational amplifiers (CFOAs). The CFOA includes a novel offset compensation technique. A digital building block is implemented in the proposed band-pass filter to tune its central frequency. An important feature of the adopted tuning procedure is the ability to tune the filter without affecting other characteristics such as gain, phase and quality factor. The band-pass filter topology is validated with a configuration where the central frequency is tuned from 60 MHz to 95 MHz with frequency steps of 5 MHz. Measurements of the offset-compensated CFOA are promising, and simulation results of the CFOA-based band-pass filter using the 0.18 ? m CMOS process confirm our theoretical analysis.

A large variety of problems in digital system design, combinational optimization and verification can be formulated in terms of operations performed on Boolean functions. The time complexity of Binary Decision Diagram (BDD) representing a Boolean function is directly related to the path length of that BDD. In this paper we present a method to generate a BDD with minimum path length. The Average Path Length (APL) and Longest Path Length (LPL) of the BDD are evaluated and discussed. The proposed method analyses the essentiality of a given variable order based on the complexity of sub functions derived from variable substitution. The variable that produces minimal cumulative complexity for the sub-functions is given priority over other variables. The experimental results and comparisons using benchmark circuits show that the proposed method is an encouraging approach towards minimizing the evaluation time of Boolean functions, consequently minimizing the time complexity of BDDs.

Modern wireless communication standards that support high rates of voice and video streaming need high-speed Analog-to-Digital Converters (ADCs) with wide Spurious-Free Dynamic Range (SFDR). Conventional time-interleaved ADCs suffer from spurious components that seriously affect the SFDR. In this paper, we present the mathematical background describing the effect of randomizing the samples among the interleaved ADCs and we propose a digitally oriented method based on this analysis to randomize the mismatches among the ADC channels. Analyses and simulations show the effectiveness of the proposed approach in multi-channel ADCs with arbitrary bit resolution, channel's number and sampling rate. For a 10-bit 500 MS/s ADC, the SFDR achieved using the proposed randomizing method can be as wide as 75 dB, which is an enhancement of more than 26 dB comparing to the conventional time interleaved ADC.

International audience; This article presents an intelligent fuzzy logic controller (FLC) for controlling single-body heaving wave energy converter (WEC) or what is widely known as “Point Absorber”. The controller aims at maximizing the energy captured from the sea waves. The power take-off (PTO) limitations are addressed implicitly in the fuzzy inference system (FIS) framework. In order to enhance the WEC power capturing bandwidth and make it less susceptible to wave environment irregularities and the system parametric uncertainties, the controller is built to have a self-configurable capability. This also eliminates the need to repeatedly run in-situ tuning procedure of the fuzzy controller or switch between several controllers based on the operating conditions. The fuzzy membership functions (MFs) are optimally tuned using particle swarm optimization (PSO) algorithm. To alleviate the computational burden associated with performing on-line optimization, the fuzzy controller is tuned at a rate significantly lower than the system sampling time. The suggested PSO-FLC has shown promising results compared with the fixed structure fuzzy logic controller (FS-FLC) and other passive control strategies. Several computer simulations were carried out to evaluate the controller effectiveness by applying different sea-states and analyzing the resultant WEC dynamics.

This paper investigates the application of function based model predictive control (MPC) approach to control the movement of single-body heaving wave energy converter (WEC). The proposed controller is designed based on the principle of maximum power transfer, at which maximum power absorption is attained. In addition, the suggested MPC controller respects the physical and thermal limitations of the WEC. Also, the generalized architecture of the proposed controller allows for longer prediction horizons and permits to effectively tune the controller based on the surrounding wave environment. Simulations have been carried out to assess the performance of the proposed controller using realistic sea states. Results show that the proposed MPC controller has achieved satisfactory performance allowing the WEC to optimize energy absorption while respecting the system limitations.

This study addresses a low computation cost controller for maximizing energy absorption of a point absorber in wave energy converters (WEC). The objective can be achieved by ensuring the perfect tracking between the buoy’s velocity and its reference. In particular, the sliding mode control (SMC) is proposed for achieving the maximum energy in WEC. The SMC is considered to be a low cost controller because it is a fixed controller and tuned offline. In order to prove the effectiveness of the SMC, a computer simulation is conducted using the actual polychromatic ocean wave. The proposed controller is tested under the nominal case and its perturbations. The perturbation scenario considers the un-model dynamic of the losses force and perturbation in the excitation force. For the sake of comparison, another low cost controller so called passive reactive controller is simulated under the same condition with the proposed SMC.

This paper describes a CMOS building block dedicated to high performance mixed analog-digital circuits and systems. The circuit consists of six MOS transistors realizing a new wideband and tunable transconductance. The theory of operation of this device is presented and the effects of transistor nonidealities on the global performances are investigated. Use of the proposed circuit to realize tunable functions (Gm-C filter and current opamp) is illustrated. HSPICE simulations show a wide tuning range of the transconductance value from 40 μS to 950 μS (500 μS) for ±2.5 V (±1.5 V) supply voltages. The transconductance value remains constant up to frequencies beyond 500 MHz. The bandpass filter built with few transconductance blocks and capacitances was simulated with ±2.5 V supply voltage, the center frequency is tunable in the range of 30 MHz to 110 MHz. However, the opamp, which is designed with a transresistance-transconductance architecture, was simulated with ±1.5 V supply voltage. The gain of the opamp can be tuned between 70 dB and 96 dB and high gain-bandwidth product of 145 MHz has been achieved at power consumption of less than 0.5 mW. Experimental results on a fabricated transconductor chip are provided.

This work describes a new CMOS current-feedback operational amplifier (CFOA) with an on-chip continuous-time current-mode input offset voltage compensation circuit. The proposed compensation method is based on a combination of two techniques: the error integration and the current feedback. In addition, this method is irrespective of process and temperature parameters because of its fully symmetrical architecture. HSPICE simulations of the designed CMOS CFOA layout show that the input offset voltage could be reduced to lass than 1 mV, and a gain of around 112 dB and a power consumption of less than 3 mW are achievable.

This work discusses a waste heat recovery system applied to chimneys allowing to heat water in residential buildings. A prototype illustrating the idea is implemented and tested. Different waste heat scenarios by varying the quantity of burned firewood (heat input) are experimented. Temperature measurements are performed at different parts of the heat recovery system. Gas flow rates of the Exhaust pipes of the system are also measured. Using this system, measurements showed that a tank of 95 L of water can be heated up to 78 °C within one hour. Obtained results show that the convection and radiation exchanges at the bottom surface of the tank have a considerable impact on the total heat transfer rate of the water (up to 70%) used in the prototype under test.

Many articles on the status of renewable energy in Lebanon have been published recently and this paper comes as a modest contribution to what has been said till now emphasizing the importance of keeping the renewable energy strategy adopted by the current and previous Lebanese governments. This paper sheds light on the current status of renewable energy in Lebanon, problems opposing its implementation, and suggestions to improve this unavoidable choice.

This paper proposes two control strategies for controlling the heaving wave energy converter (WEC): sliding mode control (SMC) and fuzzy logic control (FLC). The two methods are implemented to maximize the absorbed energy of the heaving WEC. This can be achieved by designing the control laws so that the buoy's velocity follows its reference. The two control strategies are tested using actual polychromatic ocean wave. The control strategies consider the physical limitation of the control force and the buoy's elevation. Finally, the performances of the two control methods are tested and compared in the nominal and perturbation scenarios.

Soldering techniques used to interconnect large thin c-Si solar cells in PV modules have a significant impact on the generated electrical power and reliability of the manufactured modules. Poor soldering process results on an abnormal mechanical or thermal stress which leads to micro cracks, cell breakage, and lower adhesion force. Moreover, poor soldering increases the contact resistance (Rc) between the busbar and ribbon and lowers the fill factor (FF) of the PV module. In this paper, the impact of process parameters in an automated induction soldering process has been examined. Different adhesion force (AF) profiles and relationship with current transport mechanism have been investigated. AF is increased with temperature till 240-260°C whereas it is lowered at low and very high soldering temperature. Electroluminescence (EL) image showed that solar cell is prone to micro-cracks at higher temperature but observed as a random event. Modules were processed with different soldering conditions and characterized. Results show that, using the suggested soldering process, FF can be improved by 0.53, and the power output can be as high as 251.26 W, which is higher by 2.50% compared to the non-optimized process.

The geographical location (Latitude: 24 deg 28′ N and Longitude: 54 deg 22′ E) of Abu Dhabi city in the United Arab Emirates (UAE) favors the development and utilization of solar energy. This paper presents an artificial neural network (ANN) approach for the estimation of monthly mean global solar radiation (GSR) on a horizontal surface in Abu Dhabi. The ANN models are presented and implemented on a 16-yr measured meteorological data set for Abu Dhabi comprising the maximum daily temperature, mean daily wind speed, mean daily sunshine hours, and mean daily relative humidity between 1993 and 2008. The meteorological data between 1993 and 2003 are used for training the ANN and data between 2004 and 2008 are used for testing the estimated values. Multilayer perceptron (MLP) and radial basis function (RBF) are used as ANN learning algorithms. The results attest to the capability of ANN techniques and their ability to produce accurate estimation models.

This paper presents an artificial neural network (ANN) model for predication global solar radiation (GSR) for main cities in the UAE namely, Abu Dhabi, Al-Ain and Dubai. Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) techniques with comprehensive training algorithms, architectures, and different combinations of inputs are used to develop these models. The measured data include the maximum temperature (°C), mean wind speed (knot), sunshine hours, mean relative humidity (%) and mean daily global solar radiation on a horizontal surface (kWh/m2). This data was provided by the National Center of Meteorology and Seismology (NCMS) of Abu Dhabi. The results show the generalization capability of ANN approach and its ability to generate accurate prediction of GSR in UAE.

This paper aims at presenting the experience of the renewable energy group at the United Arab Emirates University (UAEU) in the organization of the “Engineering Students Renewable Energy Competition (ESREC)” for undergraduate engineering and technical college students in the United Arab Emirates (UAE). The benefits of such competition to students are that it allows them to use and apply the technical knowledge gained during their engineering formation in the design of a renewable energy (RE) system. The paper presents the evolution of the ESREC competition over four cycles, its judging process and the results.

This paper describes different kinds of radiating structures, presents the current electromagnetic radiations measurement techniques, and proposes a complementary measurement range adapted for large dimensions antennas, and allows potential gain in measurement time and costs compared to indoor measurement ranges.

This paper considers sliding mode controller (SMC) for heaving wave energy converter (WEC). The proposed SMC is implemented to maximize the absorbed energy of the heaving WEC. The problem of chattering control force is solved via the approximation method using the saturation function. The proposed method is tested using polychromatic sea state to simulate the real sea environment. A large difference in energy absorption is resulted using the proposed method compare to a freely motion of heaving WEC. The proposed method maintains the excursion of the buoy within the acceptable limit. The proposed method can maintain the maximum energy absorption despite the presence of uncertainty in its model parameter.

This paper proposes a new reactive control technique for controlling single body heaving wave energy converter (WEC). A function-based continuous model predictive controller (MPC) is implemented to maximize the absorbed energy while respecting the physical limitations of the device. Laguerre orthonormal functions are deployed to approximate the input variable trajectory, which reduces the computational burden associated with MPC. The developed control strategy is evaluated using both monochromatic and polychromatic sea states. The function-based mode predictive controller showed decent reference signal tracking capabilities, optimizing the control effort to achieve maximum power transfer, while respecting the actuators mechanical and electrical limitations. Also the controller provides means to alleviate the problem of excessive real/reactive power flows.

The geographical location of the United Arab Emirates (UAE) (latitude between 26° and 32° North and longitude between 51° and 56° East) favors the development and utilization of solar energy. This chapter presents estimation models for the global solar radiation (GSR) in Dubai, UAE. It compares between six empirical regression models and the best of 11 different configurations of artificial neural network (ANN) models. The models have been developed using measured average daily GSR data for 7 years (2002–2008) while the measured data for the years 2009–2010 are used for testing the models. Results of monthly average daily GSR data of all the empirical models for the test period 2009–2010 yield low statistical error parameters and coefficients of determination (R2) better than 96 %. Comparison with ANN models and Solar Radiation (SoDa) Web site data shows that the optimal multilayer perceptron (MLP) ANN model is the best with R2 = 98 %, and with the lowest statistical error parameters. The results also confirm that a simple linear regression model provides a very good estimation for monthly and daily average GSR data.

This paper thermo-physically characterizes salt hydrate, paraffin wax and milk fat as phase change materials (PCMs). The three PCMs are compared in terms of improving heat sink (HS) performance for cooling electronic packaging. An experimental study is carried out on commercially available finned HS with and without PCM under natural ventilation (NV) and forced ventilation (FV) at different heat loads (4 W to 10 W). The results indicate that integration of all of the PCMs into the HS improves its cooling performance; however, milk fat lags behind the other two PCMs in terms of cooling produced. A three-dimensional pressure-based conjugate heat transfer model has been developed and validated with experimental results. The model predicts the parametric influence of PCM melting range, thermal conductivity and density on HS thermal management performance. The HS cooling performance improves with increased density and conductivity while it deteriorates with the wider melting range of the PCMs.

Shading and overheating of photovoltaic cells can result in a significant energy reduction of PV systems. Tilting and natural ventilation allows the buildup of fine sand to be blown off from the PV array's surface. However, dust particles tend to gradually accumulate on the PV surface making the cleaning task more difficult. The accumulated dust reduces the overall PV panel efficiency due to the combined effect of shading and heating. In this research work an original method to reduce effect of windblown sand and dust and increased PV temperature on photovoltaic arrays is described. The proposed method consists of removing air blown dust from photovoltaics using forced air flow of cooled return air from existing air conditioning systems.

This paper presents a new control strategy based on fuzzy logic for maximizing the absorbed energy of heaving wave energy converters (WEC). The fuzzy logic controller is responsible of minimizing the discrepancy between the optimal reference buoy velocity and the actual buoy velocity, hence maximizing the captured power for irregular sea environment. The proposed controller performed well compared to a conventional fixed reactive control strategy.

Multicrystalline silicon (mc-Si) material is a promising alternative to monocrystalline CZ silicon because of its lower cost. Solar cell industries are investing in the improvement of electrical performance of mc-Si to make it competitive in the solar cell market. To improve the absorption of incident light, an isotropic texture using nitric acid (HNO 3 ), hydrofluoric acid (HF) and demineralized (i.e. dionized) water (DI H 2 O) is widely used but leads to etch pits at the grain boundary. In this research work, the formation of etch pits and its impact on the electrical performance of the solar cell has been analyzed. In addition, phosphorus diffusion temperature, phosphorus concentration, coating thickness (CT), refractive index (RI) of the anti reflection coating (ARC), and sintering speed of metal electrodes have been investigated. A batch of 156 mm2(SQ) is fabricated with 16.54% average cell efficiency which is 0.42%absolute higher and the shunt resistance (R sh ) is increased by two fold compared to the standard process. The surface morphology, reflectance factor (RF), open circuit voltage (V oc ), short circuit current (I sc ), fill factor (FF), and cell efficiency (η) have been analyzed and compared also with the standard process.

This paper proposes a new suboptimal reactive control technique for controlling single body heaving wave energy converter (WEC). A self-tunable fuzzy logic controller is implemented to maximize the absorbed energy while respecting the physical limitations of the device. Particle swarm optimization (PSO) algorithm is utilized to carry out an on-line optimization process of the fuzzy membership functions (MFs) embedded in the fuzzy controller. The proposed PSO-FLC out-performed the conventional fuzzy logic controller (C-FLC) in terms of improving reference signal tracking accuracy, widening the device wave absorption bandwidth, and preserving a degree of robustness to model uncertainties.

The prediction of the global solar radiation is of great importance for several engineering applications. Just to name few, solar radiation estimates are fundamental during the design phase of several technologies such as: flat plates and concentrating collectors, solar energy storage devices, solar heaters, and photovoltaic systems. The geographical location of the United Arab Emirates (UAE) (between 26° and 32° North Latitude and between 51° and 56° 25 East Latitude) favors the development and utilization of solar energy. A graphical user interface (GUI) was designed to assist solar energy planners, engineers, architectural scientists, and researchers to predict global solar radiation in different regions of the UAE. The predicted models are developed using Artificial Neural Network techniques.

The availability of short-term forecast weather model for a particular country or region is essential for operation planning of energy systems. This paper presents the first step by a group of researchers at UAE University to establish a weather model for the UAE using the weather data for at least 10 years and employing various models such as classical empirical models, artificial neural network (ANN) models, and time-series regression models with autoregressive integrated moving-average (ARIMA). This work uses time-series regression with ARIMA modeling to establish a model for the mean daily and monthly global solar radiation (GSR) for the city of Al-Ain, United Arab Emirates. Time-series analysis of solar radiation has shown to yield accurate average long-term prediction performance of solar radiation in Al-Ain. The model was built using data for 10 years (1995–2004) and was validated using data of three years (2005–2007), yielding deterministic coefficients (R2) of 92.6% and 99.98% for mean daily and monthly GSR data, respectively. The low corresponding values of mean bias error (MBE), mean absolute bias error (MABE), mean absolute percentage error (MAPE), and root-mean-square error (RMSE) confirm the adequacy of the obtained model for long-term prediction of GSR data in Al-Ain, UAE.

Many researchers have modeled weather data using classical regression, time-series regression and Artificial Neural Networks (ANN) techniques. MATLAB is used in handling data and writing task specific codes for models as well as in performing statistical analysis and curve fitting works. This is due to the dynamic nature of MATLAB and its rich toolboxes that cover almost every aspect of mathematical and statistical engineering applications. Numerous authors (Abdalla & Feregh, 1988; Assi & Jama, 2010; Akinoglu & Ecevit, 1990; Al Mahdi et al., 1992; Ampratwum & Drovlo, 1999; Elagib & Mansell, 2000; Khalil & Alnajjar, 1995; Menges et al., 2006; Newland, 1988; Podesta’ et al., 2004; Sahin, 2007; Samuel, 1991; Ulgen & Hepbasli, 2002) to count few developed empirical regression models to predict the monthly average daily global solar radiation (GSR) in their region using various parameters. The mean daily sunshine duration was the most commonly used and available parameter. The most popular model was the linear model by Angstrom-Prescott (Podesta’ et al., 2004; Assi & Jama, 2010) which establishes a linear relationship between GSR and sunshine duration with knowledge of extra-terrestrial solar radiation and the theoretical maximum daily solar hours. Many studies with empirical regression models were done for diverse regions around the world. (Menges et al. 2006) reviewed 50 GSR empirical models available in literature for computing the monthly average daily GSR on a horizontal surface. They tested the models on data recorded in Konya, Turkey for comparison of model accuracy. The number of weather parameters varied between models. The diverse regression models used include linear, logarithmic, quadratic, third order polynomial, logarithmic-linear and exponential and power models relating the normalized GSR to normalized sunshine hours. Other models included in Menges work used direct regression models involving various weather parameters such as precipitation, cloud cover, etc., in addition to geographical data (altitude, latitude). (Sahin, 2007) presented a novel method for estimating the solar irradiation and sunshine duration by incorporating the atmospheric effects due to extraterrestrial solar irradiation and length of day. The author compares his model with Angstrom’s equation with favourable advantages as his method does not use Least Square Method in addition to having no procedural restrictions or assumptions. (Ulgen & Hepbasli, 2002) developed two empirical correlations to estimate the monthly average daily GSR on a horizontal surface for Izmir, Turkey. Their models resemble Angstrom type equations. They

Information about the availability of solar radiation on horizontal surface is essential for the optimum design and study of solar energy systems. For a country like UAE, the efficient application of solar energy seems inevitable because of abundant sunshine available throughout the year. The traditional way of knowing the amount of global solar radiation (GSR) in a particular region is to install pyranometers at as many locations as possible in this region thus requiring daily maintenance and data recording, and consequently increasing cost of GSR data collection. Therefore, it is rather more economical to develop methods to estimate the GSR using climatological parameters (Akhlaque et al., 2009; Kassem et al., 2009; Falayi et al., 2008; El-Sebaii & Trabea, 2005). Many researchers estimated global solar radiation by using artificial neural networks. (Mohandes et al, 1998) applied ANN techniques to predict GSR using weather data from 41 stations in Saudi Arabia. Data from 31 stations was used in training the NN and the remaining data was used for testing. Input variables to the NN included 4 parameters: latitude, longitude, altitude and sunshine duration. Their sample data was not large enough to allow a credible comparison between the ANN models used and empirical regression models. (Lam et al., 2008) have used Artificial Neural networks ANNs to develop predication models for daily global solar radiation using measured sunshine duration for 40 cities covering 9 major thermal climatic zones and sub-zones in China. (Alam et al., 2009) used ANNs to estimate monthly mean hourly and daily diffuse solar radiation based on weather data from 10 Indian stations which have different climatic conditions. (Alawi & Hinai, 1998) applied ANNs to predict solar radiation in areas not covered by direct measurement instrumentation. The input data that was used for building the network were the location, month, mean pressure, mean temperature, mean vapor pressure, mean relative humidity, mean wind speed and mean duration of sunshine. (Tasadduq et al., 2002) have used neural networks for the prediction of hourly mean values of ambient temperature 24 hours in advance. Full year hourly values of ambient temperature are used to train a neural network model for a coastal location — Jeddah, Saudi Arabia. (Elminir et al., 2005) applied ANN modeling techniques to predict solar radiation data in different spectrum bands from data of meteorology for Helwan (Egypt) meteorology monitoring station. (Rehman & Mohandes, 2008) developed ANN-based estimation GSR for Abha city in Saudi Arabia; by

This paper presents a MATLAB-based tool for the design of PV systems. The proposed modeling tool uses daily weather data and simple irradiance models that are as accurate as many other complex models. This tool is flexible enough to adapt quickly if other locations in the world are to be investigated. Besides, the tool offers a variety of PV products such as modules, inverters, cables, etc. The tool is still under development to include more features such as the prediction of the amount of CO2 that can be reduced in case of replacing a traditional building by a BIPV.

Planning a photovoltaic (PV) system is complex. Many different factors must be taken into account such as site information, module type and inclination of the modules, level of irradiation, choice of the most suitable inverter, etc. To ease the planning process, computer based tools must be utilized. In this paper, a MATLAB-based modeling tool to design, predict and analyze grid - tied PV systems is presented. This software has a user friendly interface which requires a minimum of data in order to present your optimum system and examine its performance in a clear graphical form. The user can explore different combinations and scenarios to meet the proposed design requirements. Simplicity, accuracy and ability to adapt the designed tool to any updates in both site information and system components would probably make it an excellent tool for educational and research purposes.