Global Solar Radiation Prediction in Colombia Using a Backpropagation Neural Network Architecture
(*) Corresponding author
The main source of renewable energy available in nature is solar radiation, which is the most promising resource to replace non-renewable energy sources and reduce gas emissions into the atmosphere since it allows various forms of capture and transformation through photovoltaic and photothermal systems. For an optimum use of solar energy, it is necessary to characterize and know the solar radiation at the level of the earth's surface, but this varies with time instantaneously, hourly, daily, and during seasons, with the latitude and with the local microclimates of the site. Therefore, a backpropagation artificial neural network (ANN) has been used to develop a mathematical model to predict solar radiation and the polycrystalline temperature, as a function of the ambient in the Colombian territory, specifically in the Atlantic coast. The network has been trained with 300 of the 381 data that constituted the matrix to obtain the RMSE that has been 0.164, with a network architecture composed of 10 layers and 5 neurons per layer. In addition, it has been used as a learning constant of 0.5 for each interconnection of the ANN. The increase in the number of hidden layers and the number of neurons increases the network performance, improving the prediction of the objective variable around 13% when using an architecture with five neurons per layer (NL), and 15 numbers of layers (L). In general, the results obtained have shown an acceptable performance of the artificial neural network in the estimation of solar radiation, but with certain possibilities of being improved.
Copyright © 2019 Praise Worthy Prize - All rights reserved.
M. R. Shaikh, S. Shaikh, S. B Waghmare, S. Labade, and A. Tekale, A Review Paper on Electricity Generation from Solar Energy, Int. J. Res. Appl. Sci. Eng. Technol., vol. 5, no. 9, pp. 1884-1889, 2017.
M. I. Al-Najideen and S. S. Alrwashdeh, Design of a solar photovoltaic system to cover the electricity demand for the faculty of Engineering- Mu’tah University in Jordan, Resour. Technol., vol. 3, no. 4, pp. 440–445, 2017.
H. Alberto Ortiz Diaz, C. Escobar, and S. Sepulveda, Statistical analysis of climatological variables in the City of Cúcuta, Respuestas, vol. 23, no. 1, pp. 39-44, 2018.
D. Rodríguez-Urrego and L. Rodríguez-Urrego, Photovoltaic energy in Colombia: Current status, inventory, policies, and future prospects, Renew. Sustain. Energy Rev., vol. 92, pp. 160–170, 2018.
M. Fernando Ariza Taba, M. Mwanza, N. Çetin, and K. Ulgen, Assessment of the energy generation potential of photovoltaic systems in Caribbean region of Colombia, Period. Eng. Nat. Sci., vol. 5, no. 1, pp. 55-60, 2017.
A. Aghahosseini, D. Bogdanov, L. S. N. S. Barbosa, and C. Breyer, Analysing the feasibility of powering the Americas with renewable energy and inter-regional grid interconnections by 2030, Renew. Sustain. Energy Rev., vol. 105, pp. 187–205, 2019.
N. F. Marrugo Cardenas, Prediction of Solar Global Radiation in Bogotá Colombia Based on Mathematical Models, Int. J. Smart Home, vol. 9, no. 12, pp, 91-100, 2015.
E. Noriega Angarita, V. Santos, M. Quintero-Duran, and C. Gil-Arrieta, Solar Radiation Prediction for Dimensioning Photovoltaic Systems Using Artificial Neural Networks, Int. J. Eng. Technol., vol. 8, pp. 1817-1825, 2016.
A. Teke, H. B. Yıldırım, and Ö. Çelik, Evaluation and performance comparison of different models for the estimation of solar radiation, Renew. Sustain. Energy Rev., vol. 50, pp. 1097-1107, 2015.
A. Qazi, H. Fayaz, A. Wadi, R. G. Raj, N. A. Rahim, and W. A. Khan, The artificial neural network for solar radiation prediction and designing solar systems: A systematic literature review, Journal of Cleaner Production, vol. 104, pp. 1-12, 2015.
Y. Kashyap, A. Bansal, and A. K. Sao, Solar radiation forecasting with multiple parameters neural networks, Renew. Sustain. Energy Rev., vol. 49, pp. 825-835, 2015.
T. Huang, S. Wang, Q. Yang, and J. Li, A GIS-based assessment of large-scale PV potential in China, Energy Procedia, vol. 152, pp. 1079–1084, 2018.
L. Zou, L. Wang, A. Lin, H. Zhu, Y. Peng, and Z. Zhao, Estimation of global solar radiation using an artificial neural network based on an interpolation technique in southeast China, J. Atmos. Solar-Terrestrial Phys., vol. 146, pp. 110-122, 2016.
E. F. Alsina, M. Bortolini, M. Gamberi, and A. Regattieri, Artificial neural network optimisation for monthly average daily global solar radiation prediction, Energy Convers. Manag., vol. 120, pp. 320-329, 2016.
B. Alluhaidah, S. Shehadeh, and M. El-Hawary, Most Influential Variables for Solar Radiation Forecasting Using Artificial Neural Networks, IEEE Electrical Power and Energy Conference, pp. 71-75, 2014.
V.A. Espana, A.R. Pinilla, P. Bardos, and R. Naidu, Contaminated land in Colombia: a critical review of current status and future approach for the management of contaminated sites, Science of the Total Environment, vol. 618, pp. 199-209, 2018.
M. Jiménez, L. Cadavid, and C. J. Franco, Scenarios of photovoltaic grid parity in Colombia, DYNA, vol. 81, pp. 237–245, 2014.
D. López-García, A. Arango-Manrique, and S. X. Carvajal-Quintero, Integration of distributed energy resources in isolated microgrids: The Colombian paradigm, TecnoLógicas, vol. 21, pp. 13–30, 2018.
M. Hajizadegan, M. Sakhdari, L. Zhu, Q. Cui, H. Huang, M. Cheng, and P. Chen, Graphene Sensing Modulator: Toward Low-Noise, Self-Powered Wireless Microsensors, IEEE Sensors Journal, vol. 17, no. 22, pp. 7239-7247, 2017.
S. Ghimire, R.C. Deo, N.J. Downs, and N. Raj, Global solar radiation prediction by ANN integrated with European Centre for medium range weather forecast fields in solar rich cities of queensland Australia, Journal of cleaner production, vol. 216, pp. 288-310, 2019.
S. Samadianfard, A. Majnooni-Heris, S.N. Qasem, O. Kisi, S. Shamshirband, and K.W. Chau, Daily global solar radiation modeling using data-driven techniques and empirical equations in a semi-arid climate, Engineering Applications of Computational Fluid Mechanics, vol. 13, no. 1, pp. 142-157, 2019.
B. Qian, Q. Jing, X. Zhang, J. Shang, J. Liu, H. Wan, and R. De Jong, Adapting estimation methods of daily solar radiation for crop modelling applications in Canada, Canadian Journal of Soil Science, vol. 99, pp. 1-15, 2019.
S.S. Zanetti, R.E. Dohler, R.A. Cecílio, J.E.M. Pezzopane, and A.C. Xavier, Proposal for the use of daily thermal amplitude for the calibration of the Hargreaves-Samani equation, Journal of hydrology, vol. 571, pp. 193-201, 2019.
Narváez Argoty, F., Lyons, A., Sierra Vargas, F., Neural Network Model to Predict Exhaust Emissions on a Stationary Diesel Engine Operating with Castor-Oil-Plant Biodiesel Fuel, (2017) International Review of Mechanical Engineering (IREME), 11 (2), pp. 151-160.
Soepangkat, B., Norcahyo, R., Pamuji, D., Lusi, N., Multi-Objective Optimization in End Milling Process of ASSAB XW-42 Tool Steel with Cryogenic Coolant Using Grey Fuzzy Logic and Backpropagation Neural Network-Genetic Algorithm (BPNN-GA) Approaches, (2018) International Review of Mechanical Engineering (IREME), 12 (1), pp. 42-54.
- There are currently no refbacks.
Please send any question about this web site to firstname.lastname@example.org
Copyright © 2005-2023 Praise Worthy Prize