Assessing the performance of global solar radiation empirical formulations in Kampala, Uganda

Mubiru, J.; Banda, E. J. K. B.; D’ujanga, Florence Mutonyi; Senyonga, T.

doi:10.1007/s00704-005-0196-2

Cited by 33 publications

(19 citation statements)

References 28 publications

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“…The best performing model was determined using a ranking method proposed by Mubiru et al [16]. The MBE and RMSE were calculated and normalized by dividing each by the mean of the actual dataset.…”

Section: Error Analysismentioning

confidence: 99%

Predicting Global Solar Radiation Using an Artificial Neural Network Single‐Parameter Model

Angela

Ssenyonga

James

2011

Advances in Artificial Neural Systems

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We used five years of global solar radiation data to estimate the monthly average of daily global solar irradiation on a horizontal surface based on a single parameter, sunshine hours, using the artificial neural network method. The station under the study is located in Kampala, Uganda at a latitude of 0.19 • N, a longitude of 32.34 • E, and an altitude of 1200 m above sea level. The five-year data was split into two parts in 2003-2006 and 2007-2008; the first part was used for training, and the latter was used for testing the neural network. Amongst the models tested, the feed-forward back-propagation network with one hidden layer (65 neurons) and with the tangent sigmoid as the transfer function emerged as the more appropriate model. Results obtained using the proposed model showed good agreement between the estimated and actual values of global solar irradiation. A correlation coefficient of 0.963 was obtained with a mean bias error of 0.055 MJ/m 2 and a root mean square error of 0.521 MJ/m 2 . The single-parameter ANN model shows promise for estimating global solar irradiation at places where monitoring stations are not established and stations where we have one common parameter (sunshine hours).

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Section: Error Analysismentioning

confidence: 99%

Predicting Global Solar Radiation Using an Artificial Neural Network Single‐Parameter Model

Angela

Ssenyonga

James

2011

Advances in Artificial Neural Systems

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“…Also, the number of global policies and targets to support the development of solar energy technologies has been increasing rapidly. Nevertheless, despite such great advances in solar energy applications, the unavailability of measured solar radiation data, due to high costs required to purchase and calibrate the instruments, is still a huge barrier to develop the solar energy systems properly and efficiently in many parts of the globe (Mubiru et al 2007;Wu et al 2012;Shamim et al 2015). As a consequence, collecting and processing precise solar radiation data, especially in the form of horizontal global solar radiation, are of vital importance.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Day of the year-based prediction of horizontal global solar radiation by a neural network auto-regressive model

Gani

Mohammadi

Shamshirband

et al. 2015

Theor Appl Climatol

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The availability of accurate solar radiation data is essential for designing as well as simulating the solar energy systems. In this study, by employing the long-term daily measured solar data, a neural network auto-regressive model with exogenous inputs (NN-ARX) is applied to predict daily horizontal global solar radiation using day of the year as the sole input. The prime aim is to provide a convenient and precise way for rapid daily global solar radiation prediction, for the stations and their immediate surroundings with such an observation, without utilizing any meteorological-based inputs. To fulfill this, seven Iranian cities with different geographical locations and solar radiation characteristics are considered as case studies. The performance of NN-ARX is compared against the adaptive neurofuzzy inference system (ANFIS). The achieved results prove that day of the year-based prediction of daily global solar radiation by both NN-ARX and ANFIS models would be highly feasible owing to the accurate predictions attained. Nevertheless, the statistical analysis indicates the superiority of NN-ARX over ANFIS. In fact, the NN-ARX model represents high potential to follow the measured data favorably for all cities. For the considered cities, the attained statistical indicators of mean absolute bias error, root mean square error, and coefficient of determination for the NN-ARX models are in the ranges of 0.44-0.61 kWh/m 2 , 0.50-0.71 kWh/m 2 , and 0.78-0.91, respectively.

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“…The performance of the models were evaluated based on dimensionless error statistics called rank scores and coefficients of determination. Using the rank score approach suggested by Mubiru et al [28], temperatue based ANN model performed better than the temperature based empirical model for the locations considered. This reaffirms the fact that minimum and maximum ambient temperatures are the most important weather parameters for predicting monthly average global solar radiation.…”

Section: Discussionmentioning

confidence: 98%

“…The best performing model was determined using a ranking method proposed by Mubiru et al [28]. Following their procedure, a rank score was computed as the sum of both normalized M BE and the normalized RM SE.…”

Section: Ann Model Developmentmentioning

confidence: 99%

Comparative Assessment of Temperature Based ANN and Angstrom Type Models for Predicting Global Solar Radiation

Egeonu

Njoku

Okolo

et al. 2015

Advances in Intelligent Systems and Computing

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Abstract. In this study, temperature based artificial neural network (ANN) models and Angstrom type models for predicting global solar radiation were developed for selected locations in Nigeria.The ANN models were standard multi-layered feed forward, back-propagation neural networks trained with the Levenberg Marquardt algorithm using seventeen years data collected from Nigerian Meteorological Agency (NIMET ), Abuja, Nigeria and tested with twenty-two years monthly averaged data downloaded from National Aeronautical Space Administration (NASA) online database. The network inputs were latitude, longitude, elevation, month, maximum ambient temperature (Tmax) and minimum ambient temperature (Tmin), while monthly average global solar radiation was the network output.The Angstrom type empirical models correlated global solar radiation with minimum and maximum ambient temperatures. The performance of the models were evaluated using statistical performance indicators, namely RM SE, MBE, R 2 and rank score. The coefficients of determination (R 2 ) of the ANN models were always greater than 99% for all the selected locations while the highest coefficient of determination for the empirical models was 89%. The temperature-based ANN models were thus shown to deliver superior and more reliable outcomes in comparison with the empirical models.

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Assessing the performance of global solar radiation empirical formulations in Kampala, Uganda

Cited by 33 publications

References 28 publications

Predicting Global Solar Radiation Using an Artificial Neural Network Single‐Parameter Model

Predicting Global Solar Radiation Using an Artificial Neural Network Single‐Parameter Model

RETRACTED ARTICLE: Day of the year-based prediction of horizontal global solar radiation by a neural network auto-regressive model

Comparative Assessment of Temperature Based ANN and Angstrom Type Models for Predicting Global Solar Radiation

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