The Experts below are selected from a list of 10920 Experts worldwide ranked by ideXlab platform
S. O. Enibe - One of the best experts on this subject based on the ideXlab platform.
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thermal analysis of a natural circulation solar air heater with phase change material energy storage
Renewable Energy, 2003Co-Authors: S. O. EnibeAbstract:The transient thermal analysis of a natural convection solar air heater is presented. The heater consists of a single-glazed flat plate solar collector integrated with a paraffin type phase change material (PCM) energy storage subsystem and a rectangular enclosure which serves as the working chamber. The PCM is prepared in modules, with the modules equispaced across the absorber plate. The underside of the absorber plate, together with the vertical sides of the PCM module container, serve as air heating vanes. Air flow through the system is by natural convection. Energy balance equations are developed for each major component of the heater and linked with heat and mass balance equations for the heated air flowing through the system. The airflow rate is determined by balancing the buoyancy head resulting from thermally induced density differences and the friction head due to various flow resistances. The predicted performance of the system is compared with experimental data under daytime no-load conditions over the ambient temperature range of 19–41 °C and daily Global Irradiation of 4.9–19.9 MJ m–2. Predicted temperatures at specific locations on the absorber plate, heat exchanger plate, glazing, and heated air agree closely with experimental data to within 10, 6, 8, and 10 °C, respectively. Maximum predicted cumulative useful and overall efficiencies of the system are within the ranges 2.5–13 and 7.5–18%, respectively. Correlations of the predicted efficiencies are presented.
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performance of a natural circulation solar air heating system with phase change material energy storage
Renewable Energy, 2002Co-Authors: S. O. EnibeAbstract:The design, construction and performance evaluation of a passive solar powered air heating system is presented. The system, which has potential applications in crop drying and poultry egg incubation, consists of a single-glazed flat plate solar collector integrated with a phase change material (PCM) heat storage system. The PCM is prepared in modules, with the modules equispaced across the absorber plate. The spaces between the module pairs serve as the air heating channels, the channels being connected to common air inlet and discharge headers. The system was tested experimentally under daytime no-load conditions at Nsukka, Nigeria, over the ambient temperature range of 19–41 °C, and a daily Global Irradiation range of 4.9–19.9 MJ m−2. Peak temperature rise of the heated air was about 15 K, while the maximum airflow rate and peak cumulative useful efficiency were about 0.058 kg s−1 and 22%, respectively. These results show that the system can be operated successfully for crop drying applications. With suitable valves to control the working chamber temperature, it can also operate as a poultry egg incubator.
David G. Dorrell - One of the best experts on this subject based on the ideXlab platform.
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Köppen-Geiger climate classification adjustment of the BRL diffuse Irradiation model for Australian locations
'Elsevier BV', 2021Co-Authors: Jeremy Every, Li L, David G. DorrellAbstract:© 2019 Elsevier Ltd Numerous mathematical models have been developed to estimate diffuse and direct irradiance components based on Global Irradiation measurements. The Boland–Ridley–Lauret (BRL) model consists of a single set of parameters for all Global locations. There is scope to improve the BRL model to better match local climatic conditions. In this research, the Köppen-Geiger climate classification system is considered to develop a set of adjusted BRL models for Australian conditions. Ground-based and satellite-based Irradiation data derived from the Australian Bureau of Meteorology are used to tune and test new BRL models developed at a national level and for each climate zone. Irradiation data are processed through a rigorous quality control procedure before parameter tuning. For ground-based data, a new national model results in an improvement in 96% of statistical indicators over the original BRL model while Köppen-Geiger zone adjusted models show improvement over the new national model in 72% of the statistics. For satellite-based Global Irradiation estimates, a new national BRL model also results in observed improvements, however, no discernible improvement is observed for Köppen-Geiger zone models
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Köppen-Geiger climate classification adjustment of the BRL diffuse Irradiation model for Australian locations
Renewable Energy, 2020Co-Authors: Jeremy Every, David G. DorrellAbstract:Abstract Numerous mathematical models have been developed to estimate diffuse and direct irradiance components based on Global Irradiation measurements. The Boland–Ridley–Lauret (BRL) model consists of a single set of parameters for all Global locations. There is scope to improve the BRL model to better match local climatic conditions. In this research, the Koppen-Geiger climate classification system is considered to develop a set of adjusted BRL models for Australian conditions. Ground-based and satellite-based Irradiation data derived from the Australian Bureau of Meteorology are used to tune and test new BRL models developed at a national level and for each climate zone. Irradiation data are processed through a rigorous quality control procedure before parameter tuning. For ground-based data, a new national model results in an improvement in 96% of statistical indicators over the original BRL model while Koppen-Geiger zone adjusted models show improvement over the new national model in 72% of the statistics. For satellite-based Global Irradiation estimates, a new national BRL model also results in observed improvements, however, no discernible improvement is observed for Koppen-Geiger zone models.
Christian A. Gueymard - One of the best experts on this subject based on the ideXlab platform.
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prediction and performance assessment of mean hourly Global radiation
Solar Energy, 2000Co-Authors: Christian A. GueymardAbstract:Abstract Using a large dataset of 135 stations encompassing very diverse geographic locations (82.5°N to 67.6°S) and climates, two new models are presented to predict the monthly-average hourly Global Irradiation distribution from its daily counterpart. It is found that a quadratic in the sine of solar elevation fits the data very well at all locations. Other parameters include the mean monthly clearness index, Kt, the average day length, and the daily average solar elevation. Based on this dataset, a detailed performance assessment is conducted for these new models, as well as for six models of the literature. Their respective performance is discussed, particularly with respect to the latitudinal effect. The proposed models appear to correctly predict Irradiations even for a very low sun typical of near-polar night conditions. The accurate predictions of the newly proposed ‘daily integration model’ translate into the lowest yearly-average and site-average Root Mean Square Difference, a statistic obtained by comparison with 21 722 measured hourly Irradiations. However, it is stressed that the performance of this kind of model is seriously limited by artefacts due to shading (e.g., from mountains), and, more importantly, by strong morning/afternoon radiative asymmetries due to local or climatological influences, which cannot be predicted from just daily Irradiation data.
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Mathermatically integrable parameterization of clear-sky beam and Global irradiances and its use in daily Irradiation applications
Solar Energy, 1993Co-Authors: Christian A. GueymardAbstract:A simple parameterized clear-sky short-wave irradiance model is derived from a detailed two-band physical model presented earlier. The inputs for the parameterized model (called PSIM) are the solar elevation, the amount of precipitable water (w), the Angstrom turbidity coefficient (β), the station's pressure (or its altitude), and the zonal surface albedo (for which a simple submodel is provided for North America). PSIM is intended to give accurate irradiance estimates in any atmospheric condition whenever w < 5 cm and β < 0.45. The parameterization uses a function of solar elevation that is integrable with time, so that a parameterized daily Irradiation model (called DIM) is also obtained. The seasonal variations of the daily clear-sky beam and Global Irradiations are presented for different combinations of w, β, and latitude. It is possible to use these Irradiation estimates in different applications when dealing with solar energy or climatology. For example, a simple way to derive the mean monthly apparent solar elevation or air mass is given. It is also suggested that the original Angstrom's equation (to derive the average Global Irradiation from the fraction of possible sunshine) be used more extensively with DIM. Finally, it is demonstrated (using data from Albany, NY) that the monthly average beam Irradiation may be obtained with a very simple equation from the fraction of possible sunshine and DIM, yielding more accurate estimates than the existing best-performing method.
F O Akuffo - One of the best experts on this subject based on the ideXlab platform.
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the frequency distribution of daily Global Irradiation at kumasi
Solar Energy, 1993Co-Authors: F O Akuffo, A BrewhammondAbstract:Abstract Cumulative frequency distribution curves (CDC) for daily Global Irradiation on the horizontal produced by Liu and Jordan in 1963 have until recently been considered to have universal validity. Results obtained by Saunier et al. in 1987 and Ideriah and Suleman in 1989 for two tropical locations, Ibadan in Nigeria and Bangkok in Thailand, respectively, have thrown into question the universal validity of the Liu and Jordan generalized CDC. Saunier et al. , in particular, showed that their results disagreed with the generalized CDC mainly because of differences in the values of the maximum clearness index (Kmax), as well as the underlying probability density functions. Consequently, they proposed two expressions for determining Kmax and probability densities in tropical locations. This paper presents the results of statistical analysis of daily Global Irradiation for Kumasi, Ghana, also a tropical location. The results show that the expressions of Saunier et al. provide a better description of the observations than the generalized CDC and, in particular, the empirical equation for Kmax may be valid for Kumasi. Furthermore, the results show that the values of the minimum clearness index (Kmin) for Kumasi are much higher than the generally accepted value of 0.05 for overcast sky conditions. A comparison of the results for Kumasi and Ibadan shows that there is satisfactory agreement when the values of Kmax and Kmin are comparable; in cases where there are discrepancies in the Kmax and Kmin values, the CDC also disagree.
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correlations between monthly average daily Global Irradiation and relative duration of sunshine at kumasi
Energy Conversion and Management, 1992Co-Authors: E A Jackson, F O AkuffoAbstract:Abstract The Angstrom-Page type correlation between monthly average, daily Global Irradiation on the horizontal and the monthly average relative duration of sunshine has been derived for Kumasi, Ghana, using 20 years data of measured values by the Ghana Meteorological Services Department. The correlations were carried out at three levels: for each month of the year; for the dry, wet and harmattan seasons; and for the full year. At the three levels, the maximum absolute errors in the estimated Global Irradiation are 0.59, 4.24 and 8.34%, respectively. The analyses were repeated after incorporating multiple reflections between the ground and sky and non-burning of the sunshine recorder chart when the solar elevation was below 4°. These latter considerations did not significantly affect the results.
A Khalil - One of the best experts on this subject based on the ideXlab platform.
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a profile free non parametric approach towards generation of synthetic hourly Global solar Irradiation data from daily totals
Renewable Energy, 2021Co-Authors: Muhammed A Hassan, Mohamed Abubakr, A KhalilAbstract:Abstract Solar radiation is an essential input in the design and operation of many engineering systems. However, access to high-resolution data (hourly or sub-hourly) is usually limited, especially in developing countries, either due to its unavailability or expensive costs. A novel data-driven approach is proposed to predict the hourly Global Irradiation profiles from the cheaper and more likely available records of daily Global Irradiation. The proposed approach is based on a prior categorization of hourly observations using the K-means clustering algorithm, followed by non-parametric function approximation using the multi-layered perceptron artificial neural network. This approach is applied to measured data (130,000 data points) at six locations in the North African Sahara, and the developed models are benchmarked against all existing parametric models in the literature. The artificial neural network-based models outperformed all existing models, with maximum and minimum coefficients of determination of 0.960 and 0.930, respectively. The non-parametric models also captured the true asymmetric profiles of hourly Irradiation with enhanced distributions of the residuals. Hence, the suggested models can be used to generate synthetic hourly data for multiple applications, most notably for building energy simulations and scheduling the operation of power generation systems.
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exploring the potential of tree based ensemble methods in solar radiation modeling
Applied Energy, 2017Co-Authors: Muhammed A Hassan, A Khalil, S Kaseb, M A KassemAbstract:Abstract This article provides the first comprehensive study to explore the potential of tree-based ensemble methods in modeling solar radiation. Gradient boosting, bagging and random forest (RF) models have been developed for estimating Global, diffuse and normal radiation components in daily and hourly time-scales. The developed ensemble models have been compared to their corresponding multi-layer perceptron (MLP), support vector regression (SVR) and decision tree (DT) models. The results show that the suggested techniques are very reliable and accurate, despite being relatively simple. The average validation coefficients of determination (R2) for boosting, bagging and RF algorithms are (0.957, 0.971, 0.967) for the Global Irradiation model, (0.768, 0.786, 0.791) for the diffuse Irradiation model, (0.769, 0.785, 0.792) for the normal Irradiation model, (0.852, 0.890, 0.883) for the hourly Global irradiance model, (0.778, 0.869, 0.853) for the diffuse irradiance model, and (0.797, 0.897, 0.880) for the normal irradiance model. In general, the bagging and RF algorithms showed better estimates than gradient boosting. However, the gradient boosting algorithm was the most stable with maximum increase of 10.32% in the test root mean square error, compared to 41.3% for the MLP algorithm. The SVR algorithm offers the best combination of stability and prediction accuracy. Nevertheless, its computational costs are up to 39 times the computational costs of ensemble methods. The new ensemble methods have been recommended for generating synthetic radiation data to be used for simulating and evaluating the performance of different solar energy systems.
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potential of four different machine learning algorithms in modeling daily Global solar radiation
Renewable Energy, 2017Co-Authors: Muhammed A Hassan, A Khalil, S Kaseb, M A KassemAbstract:Abstract In this study, the potential of different machine-learning algorithms in modeling Global horizontal solar Irradiation is examined. Multi-layer perceptron (MLP), adaptive neuro-fuzzy inference system (ANFIS) and Support Vector Machines (SVM) algorithms are adopted, beside a newly suggested algorithm: decision trees. All models are grouped in four categories: sunshine-, temperature-, meteorological parameters- and day number-based models. All models have been trained, optimized, validated and compared with each other and with old and newly suggested regression models, using high-resolution, highly accurate measured data recorded over Cairo, Egypt, throughout five years, as a case study. Models with best statistical measures of accuracy and best generalization abilities have been recommended after being tested using an independent dataset. The results show that MLP models excel in estimating Global Irradiation with root mean square error lower than that of best corresponding regression models by 4.75–31.69%, depending on the model category. Followed by ANFIS models (if carefully validated) and SVM models. In addition, the study assesses the ability of decision trees in modeling solar radiation. Despite of their simplicity, the merits of temperature- and day number-based models are demonstrated, with coefficients of determination greater than 85%, to be used in case of unavailability of sunshine records.