Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural Networks - 2006 special issue: Earth sciences and environmental applications of computational intelligence
2006 Special issue: Temporal neural networks for downscaling climate variability and extremes
Neural Networks - 2006 special issue: Earth sciences and environmental applications of computational intelligence
2006 Special issue: Neural network forecasts of the tropical Pacific sea surface temperatures
Neural Networks - 2006 special issue: Earth sciences and environmental applications of computational intelligence
| Hi-index | 0.00 |
This paper explores the possibility of developing a prediction model using artificial neural networks (ANNs), which could be used to estimate monthly average daily direct solar radiation for locations in Uganda. Direct solar radiation is a component of the global solar radiation and is quite significant in the performance assessment of various solar energy applications. Results from the paper have shown good agreement between the estimated and measured values of direct solar irradiation. A correlation coefficient of 0.998 was obtained withmean bias error of 0.005 MJ/m2 and rootmean square error of 0.197 MJ/m2. The comparison between the ANN and empirical model emphasized the superiority of the proposed ANN prediction model. The application of the proposed ANN model can be extended to other locations with similar climate and terrain.