WITHDRAWN: Artificial neural network-Kalman filtering approach for solar irradiation estimation

This paper presents a method which combines an artificial neural network and a genetic algorithm (ANNGA) in determining the tilt angle for photovoltaic (PV) modules. First, a Taguchi experiment was used to perform an efficient experimental design and analyze the robustness of the tilt angles for fixed south-facing PV modules. Following, the results from the Taguchi experiment were used as the learning data for an artificial neural network (ANN) model that could predict the tilt angles at discrete levels. Finally, a genetic algorithm method was applied to obtain a robust tilt angle setting of the tilt angle of PV modules with continuous variables. The objective is to maximize the electrical energy of the modules. In this study, three Taiwanese areas were selected for analysis. The position of the sun at any time and location was predicted by the mathematical procedure of Julian dating; then, the solar irradiation was obtained at each site under a clear sky. To confirm the computer simulation results, experimental system are conducted for determining the optimum tilt angle of the modules. The results show that the seasonal optimum angle is 26.4 (deg.) for February-March-April; -9.47(deg.) for May-June-July, 21.32(deg.) for August-September-October and 53.13(deg.) from November-December-January in the Taiwan area.

Download Full-text

Prediction and extrapolation of global solar irradiation on tilted surfaces from horizontal ones using an artificial neural network

3rd International Symposium on Environmental Friendly Energies and Applications (EFEA) ◽

10.1109/efea.2014.7059998 ◽

2014 ◽

Author(s):

Laidi Maamar ◽

Abdellah el hadj Abdallah ◽

Hanini Salah ◽

Rezrazi Ahmed

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Solar Irradiation ◽

Global Solar Irradiation ◽

Artificial Neural

Download Full-text

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

Advances in Artificial Neural Systems ◽

10.1155/2011/751908 ◽

2011 ◽

Vol 2011 ◽

pp. 1-7 ◽

Cited By ~ 9

Author(s):

Karoro Angela ◽

Ssenyonga Taddeo ◽

Mubiru James

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Solar Radiation ◽

Global Solar Radiation ◽

Single Parameter ◽

Solar Irradiation ◽

Global Solar Irradiation ◽

Sunshine Hours ◽

Artificial Neural ◽

Propagation Network

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/m2 and a root mean square error of 0.521 MJ/m2. 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).

Download Full-text