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Tamer Khatib - One of the best experts on this subject based on the ideXlab platform.

  • A new method for extracting I-V characteristic curve for photovoltaic modules using artificial neural networks
    2018 5th International Conference on Electrical and Electronic Engineering (ICEEE), 2018
    Co-Authors: Ahmed Ghareeb, Maan Tamimi, Mahmoud Jaber, Saif Jaradat, Tamer Khatib
    Abstract:

    This paper presents a new I-V curve prediction method using Artificial Neural Networks (ANNs), based on two ANNs, Generalized Regression Neural Network (GRNN) and cascaded forward neural network (CFNN).Solar radiation, ambient temperature, and the specification of PV module (open circuit voltage and short circuit current at STC) are inputs for this method. This method has a high accuracy in predicting I-V curves with average Mean Absolute Percentage Error (MAPE), Mean Bias Error (MBE) and Root Mean Square Error (RMSE) are 1.09%, 0.0229(A) and 0.0336(A) respectively for the validation data.

  • a novel hybrid model for hourly global solar radiation prediction using random forests technique and firefly algorithm
    Energy Conversion and Management, 2017
    Co-Authors: Ibrahim Anwar Ibrahim, Tamer Khatib
    Abstract:

    Abstract Reliable knowledge of solar radiation is an essential requirement for designing and planning solar energy systems. Thus, this paper presents a novel hybrid model for predicting hourly global solar radiation using random forests technique and firefly algorithm. Hourly meteorological data are used to develop the proposed model. The firefly algorithm is utilized to optimize the random forests technique by finding the best number of trees and leaves per tree in the forest. According to the results, the best number of trees and leaves per tree is 493 trees and one leaf per tree in the forest. Three statistical Error values, namely, root Mean square Error, Mean Bias Error, and Mean absolute percentage Error are used to evaluate the proposed model for the internal and external validation. Moreover, the results of the proposed model are compared with conventional random forests model, conventional artificial neural network and optimized artificial neural network model by firefly algorithm to show the superiority of the proposed hybrid model. Results show that the root Mean square Error, Mean absolute percentage Error, and Mean Bias Error values of the proposed model are 18.98%, 6.38% and 2.86%, respectively. Moreover, the proposed random forests model shows better performance as compared to the aforementioned models in terms of prediction accuracy and prediction speed.

  • A comparative study of evolutionary algorithms and adapting control parameters for estimating the parameters of a single-diode photovoltaic module's model
    Renewable Energy, 2016
    Co-Authors: Dhiaa Halboot Muhsen, Tamer Khatib, Abu Bakar Ghazali, Issa Ahmed Abed
    Abstract:

    This paper proposes different evolutionary algorithms, such as differential evolution and electromagnetism-like algorithms, to extract the five parameters of a single-diode photovoltaic module's model. Hybrid evolutionary algorithms are proposed with integrated and adaptive mutation per iteration schemes. In addition, a new formula to adjust the mutation scaling factor and crossover rate for each generation is proposed. Analyses are performed based on experimental data points under different weather conditions to explain the robustness and reliability of the proposed methods. Results show that the proposed hybrid algorithms, namely, evolutionary algorithm with integrated mutation per iteration and evolutionary algorithm with adaptive mutation per iteration, exhibit better performance than electromagnetism-like algorithm and other methods in terms of accuracy, CPU execution time, and convergence. The proposed hybrid algorithms offer a root Mean square Error, Mean Bias Error, coefficient of determination and CPU execution time around 0.062, 0.006 and 0.992, and less than 20 s respectively. Furthermore, the feasibility of the proposed methods is validated by comparing the obtained results with those of other methods under various statistical Errors. As a conclusion, the proposed hybrid algorithms offer root Mean square Error and Mean Bias Error less than other methods by 14% at least.

  • A Novel Approach for Solar Radiation Prediction Using Artificial Neural Networks
    Energy Sources Part A: Recovery Utilization and Environmental Effects, 2015
    Co-Authors: Tamer Khatib
    Abstract:

    This article presents a novel solar radiation prediction approach using artificial neural networks. The developed model predicts three meteorological variables using sunshine ratio, day number, and location coordinates. These meteorological variables are solar energy, ambient temperature, and relative humidity. However, three statistical values are used to evaluate the proposed model. These statistical values are Mean absolute percentage Error, Mean Bias Error, and root Mean square Error. Based on the results, the developed model predicts accurately the three meteorological variables. The Mean absolute percentage Error, root Mean square Error, and Mean Bias Error in predicting solar radiation are 1.3%, 5.8 (1.8%), and 0.9 (0.3%), respectively. While the Mean absolute percentage Error, root Mean square Error, and Mean Bias Error values for ambient temperature prediction are 1.3%, 0.4 (1.7%), and 0.1 (0.4%). In addition, the Mean absolute percentage Error, root Mean square Error, and Mean Bias Error values ...

  • Modeling of photovoltaic array output current based on actual performance using artificial neural networks
    Journal of Renewable and Sustainable Energy, 2015
    Co-Authors: Ammar Mohammed Ameen, Jagadeesh Pasupuleti, Tamer Khatib
    Abstract:

    This paper presents prediction models for photovoltaic (PV) module's output current. The proposed models are based on empirical, statistical, and artificial neural networks. The adopted artificial neural networks are generalized regression, feed forward, and cascaded forward neural networks. The proposed models have two inputs, namely, solar radiation and ambient temperature, while system's output current is the output. Two years of experimental data for a 1.4 kWp PV system are utilized in this research. These data are recorded every 10 seconds in order to consider the uncertainty of system's output current. Three statistical values are used to evaluate the accuracy of the proposed models, namely, Mean absolute percentage Error, Mean Bias Error, and root Mean square Error. A comparison between the proposed models in terms of prediction accuracy is conducted. The results show that the generalized regression neural network based model exceeds the other models. The Mean absolute percentage Error, root Mean square Error, and Mean Bias Error of the generalized regression neural network model are 4.97%, 5.67%, and −1.17%, respectively.

J Mubiru - One of the best experts on this subject based on the ideXlab platform.

  • estimation of monthly average daily global solar irradiation using artificial neural networks
    Solar Energy, 2008
    Co-Authors: J Mubiru, E J K B Banda
    Abstract:

    Abstract This study explores the possibility of developing a prediction model using artificial neural networks (ANN), which could be used to estimate monthly average daily global solar irradiation on a horizontal surface for locations in Uganda based on weather station data: sunshine duration, maximum temperature, cloud cover and location parameters: latitude, longitude, altitude. Results have shown good agreement between the estimated and measured values of global solar irradiation. A correlation coefficient of 0.974 was obtained with Mean Bias Error of 0.059 MJ/m 2 and root Mean square Error of 0.385 MJ/m 2 . The comparison between the ANN and empirical method emphasized the superiority of the proposed ANN prediction model.

  • Predicting total solar irradiation values using artificial neural networks
    Renewable Energy, 2008
    Co-Authors: J Mubiru
    Abstract:

    Abstract This study explores the possibility of developing an artificial neural networks model that could be used to predict monthly average daily total solar irradiation on a horizontal surface for locations in Uganda based on geographical and meteorological data: latitude, longitude, altitude, sunshine duration, relative humidity and maximum temperature. Results have shown good agreement between the predicted and measured values of total solar irradiation. A correlation coefficient of 0.997 was obtained with Mean Bias Error of 0.018 MJ/m2 and root Mean square Error of 0.131 MJ/m2. Overall, the artificial neural networks model predicted with an accuracy of 0.1% of the Mean absolute percentage Error.

  • Interpolating methods for solar radiation in Uganda
    Theoretical and Applied Climatology, 2006
    Co-Authors: J Mubiru, E J K B Banda, K. Karume, M. Majaliwa, T. Otiti
    Abstract:

    Existing literature lacks information on evaluation of interpolation methods for the estimation of solar radiation in the East African region. It follows that this study investigates the performance of five interpolations in Uganda which include: Nearest Point, Moving Average, Moving Surface, Trend Surface and Ordinary Kriging. Results have shown that the Moving Average linear decrease is the most appropriate for interpolation of solar radiation in Uganda and subsequent drawing of solar maps. The corresponding normalized Mean Bias Error and root Mean square Error is 0.035 and 0.078, respectively. The worst performing is the Nearest Point interpolation with normalized Mean Bias Error and root Mean square Error of 0.084 and 0.149, respectively.

K.k. Gopinathan - One of the best experts on this subject based on the ideXlab platform.

  • Solar sky radiation estimation techniques
    Solar Energy, 1992
    Co-Authors: K.k. Gopinathan
    Abstract:

    Empirical correlations suggested by various authors, for estimating monthly Mean daily diffuse irradiation, are compared statistically to test their applicability to the southern African region. The correlations are compared by calculating root Mean square Error, Mean Bias Error and Mean percentage Error. The correlations suggested by Gopinathan and Gladius Lewis are found to be most accurate for the southern African region. Equations suggested by Iqbal give poor results and cannot be employed for the region.

Zixing Lu - One of the best experts on this subject based on the ideXlab platform.

  • Mean intensity gradient an effective global parameter for quality assessment of the speckle patterns used in digital image correlation
    Optics and Lasers in Engineering, 2010
    Co-Authors: Zixing Lu
    Abstract:

    Digital image correlation (DIC) is an image-based optical metrology for full-field deformation measurement. In DIC technique, the test object surface must be covered with a random speckle pattern, which deforms together with the object surface as a carrier of deformation information. In practice, the speckle patterns may show distinctly different intensity distribution characteristics and have an important influence on DIC measurements. How to assess the overall quality of different speckle patterns with a simple yet effective parameter is an interesting but confusing problem, and is also helpful to the optimal use of the technique. In this paper, a novel, simple, easy-to-calculate yet effective global parameter, called Mean intensity gradient, is proposed for quality assessment of the speckle patterns used in DIC. To verify the correctness and effectiveness of the new concept, five different speckle patterns are numerically translated, and the displacements measured with DIC are compared with the exact ones. The Errors are evaluated in terms of Mean Bias Error and standard deviation Error. It is shown that both Mean Bias Error and standard deviation of the measured displacement are closely related to the Mean intensity gradient of the speckle pattern used, and a so-called good speckle pattern should be of large Mean intensity gradient.

Ryan Macdonald - One of the best experts on this subject based on the ideXlab platform.

  • A novel time-effective model for daily distributed solar radiation estimates across variable terrain
    International Journal of Energy and Environmental Engineering, 2018
    Co-Authors: Shaghayegh Mirmasoudi, Roland Kroebel, Daniel Johnson, James Byrne, Ryan Macdonald
    Abstract:

    Accurate and precisess estimation of spatio-temporal variability of solar radiation is critical. Some commonly used models evaluate this variability using methods in which the data required for estimating atmospheric attenuation may not be easily accessible for some study areas. Here, a daily solar radiation estimation method which uses ambient air temperature, a Digital Elevation Model, time of year, and monthly radiation estimates from Solar Analyst model has been proposed. The objective was to use air temperature-based empirical models for atmospheric transmissivity and diffuse fractions to vary total monthly radiation estimation from Solar Analyst, and then calculate total daily radiation as a fraction of total monthly radiation by applying a daily transmissivity-based ratio, as air temperature data are readily available at most locations on the planet. Results revealed that daily solar radiation can be estimated very well, with Mean Absolute Bias Error of around 40–53 W m^−2 or Mean Bias Error of ± 10%, under all sky conditions at seven sites in diverse climate regions, using significantly less input data. The presented method is an improvement over previously used methods with Mean Bias Error of under 10% but more input parameters. Furthermore, the hourly solar radiation values can be calculated using the presented method using the ratio between daily and hourly radiation, for example from literature values and estimated daily insolation. The result also showed that the method is more useful for those stations with substantially higher numbers of sunny days than cloudy or partly cloudy days because the uncertainty of the model decreased from cloudy to sunny sky conditions. The implemented Digital Elevation Models environment of this method makes it applicable in many studies that need spatial estimation of solar radiation, especially for solar energy generation projects.