TY - GEN
T1 - Application of artificial neural networks for prediction of solar radiation for Botswana
AU - Kumar, V. Sampath
AU - Prasad, J.
AU - Narasimhan, V. Lakshmi
AU - Ravi, S.
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2018/6/19
Y1 - 2018/6/19
N2 - Energy plays a pivotal role in the socio and economic development of a country. It is the key to many daily chores. The depleting fossil fuels reserve and its climatic impact on a global scale encourage the development of alternate sources of energy. It is crucial that the developing countries utilize other renewable sources and tap the potential for its development. Botswana being a landlocked country has abundance of resources and receives sunshine throughout the year. This paper discusses Artificial Neural Network Model for solar radiation in Botswana. By using data of global solar radiation, sunshine hours data recorded for six locations, Gaborone, Francistown, Kasane, Maun, Shakawe, Ghanzi the solar constants were obtained. MATLAB Neural Network tool was used for training and regression analysis computed. The results using Artificial Neural Network encourages the development of a Neural Network Model application for developing a significant model for estimating the Solar Potential in Botswana.
AB - Energy plays a pivotal role in the socio and economic development of a country. It is the key to many daily chores. The depleting fossil fuels reserve and its climatic impact on a global scale encourage the development of alternate sources of energy. It is crucial that the developing countries utilize other renewable sources and tap the potential for its development. Botswana being a landlocked country has abundance of resources and receives sunshine throughout the year. This paper discusses Artificial Neural Network Model for solar radiation in Botswana. By using data of global solar radiation, sunshine hours data recorded for six locations, Gaborone, Francistown, Kasane, Maun, Shakawe, Ghanzi the solar constants were obtained. MATLAB Neural Network tool was used for training and regression analysis computed. The results using Artificial Neural Network encourages the development of a Neural Network Model application for developing a significant model for estimating the Solar Potential in Botswana.
UR - http://www.scopus.com/inward/record.url?scp=85050140562&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85050140562&partnerID=8YFLogxK
U2 - 10.1109/ICECDS.2017.8390110
DO - 10.1109/ICECDS.2017.8390110
M3 - Conference contribution
AN - SCOPUS:85050140562
T3 - 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing, ICECDS 2017
SP - 3493
EP - 3501
BT - 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing, ICECDS 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing, ICECDS 2017
Y2 - 1 August 2017 through 2 August 2017
ER -
