Forecasting long-term global solar radiation with an ANN algorithm coupled with satellite-derived (MODIS) land surface temperature (LST) for regional locations in Queensland

Deo, Ravinesh C. and Sahin, Mehmet (2017) Forecasting long-term global solar radiation with an ANN algorithm coupled with satellite-derived (MODIS) land surface temperature (LST) for regional locations in Queensland. Renewable and Sustainable Energy Reviews, 72. pp. 828-848. ISSN 1364-0321

Abstract

Forecasting solar radiation (G) is extremely crucial for engineering applications (e.g. design of solar furnaces and energy-efficient buildings, solar concentrators, photovoltaic-systems and a site-selection of sites for future power plants). To establish long-term sustainability of solar energy, energy practitioners utilize versatile predictive models of G as an indispensable decision-making tool. Notwithstanding this, sparsity of solar sites, instrument maintenance, policy and fiscal issues constraint the availability of model input data that must be used for forecasting the onsite value of G. To surmount these challenge, low-cost, readily-available satellite products accessible over large spatial domains can provide viable alternatives. In this paper, the preciseness of artificial neural network (ANN) for predictive modelling of G is evaluated for regional Queensland, which employed Moderate Resolution Imaging Spectroradiometer (MODIS) land-surface temperature (LST) as an effective predictor. To couple an ANN model with satellite-derived variable, the LST data over 2012–2014 are acquired in seven groups, with three sites per group where the data for first two (2012–2013) are utilised for model development and the third (2014) group for cross-validation. For monthly horizon, the ANN model is optimized by trialing 55 neuronal architectures, while for seasonal forecasting, nine neuronal architectures are trailed with time-lagged LST. ANN coupled with zero lagged LST utilised scaled conjugate gradient algorithm, and while ANN with time-lagged LST utilised Levenberg-Marquardt algorithm. To ascertain conclusive results, the objective model is evaluated via multiple linear regression (MLR) and autoregressive integrated moving average (ARIMA) algorithms. Results showed that an ANN model outperformed MLR and ARIMA models where an analysis yielded 39% of cumulative errors in smallest magnitude bracket, whereas MLR and ARIMA produced 15% and 25%. Superiority of an ANN model was demonstrated by site-averaged (monthly) relative error of 5.85% compared with 10.23% (MLR) and 9.60 (ARIMA) with Willmott's Index of 0.954 (ANN), 0.899 (MLR) and 0.848 (ARIMA). This work ascertains that an ANN model coupled with satellite-derived LST data can be adopted as a qualified stratagem for the proliferation of solar energy applications in locations that have an appropriate satellite footprint.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Second place winner of the USQ Publication Excellence Award for Journal Articles published Jan-March 2017. Permanent restricted access to Published version, in accordance with the copyright policy of the publisher. The research project was supported by the USQ Academic Development and Outside Studies Program (ADOSP; July 2016 – January 2017) grant awarded to Dr RC Deo to establish research collaboration with Dr Mehmet Sahin (Department of Electrical and Electronics Engineering, Siirt University, Turkey).
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 25 Jan 2017 02:21
Last Modified: 11 Apr 2017 23:55
Uncontrolled Keywords: satellite-based solar model; Neural network; Multi-linear regression; ARIMA model
Fields of Research : 05 Environmental Sciences > 0502 Environmental Science and Management > 050204 Environmental Impact Assessment
04 Earth Sciences > 0401 Atmospheric Sciences > 040107 Meteorology
09 Engineering > 0906 Electrical and Electronic Engineering > 090608 Renewable Power and Energy Systems Engineering (excl. Solar Cells)
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080105 Expert Systems
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080110 Simulation and Modelling
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080108 Neural, Evolutionary and Fuzzy Computation
04 Earth Sciences > 0401 Atmospheric Sciences > 040103 Atmospheric Radiation
01 Mathematical Sciences > 0102 Applied Mathematics > 010204 Dynamical Systems in Applications
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970105 Expanding Knowledge in the Environmental Sciences
B Economic Development > 85 Energy > 8505 Renewable Energy > 850506 Solar-Thermal Energy
D Environment > 96 Environment > 9602 Atmosphere and Weather > 960202 Atmospheric Processes and Dynamics
Identification Number or DOI: 10.1016/j.rser.2017.01.114
URI: http://eprints.usq.edu.au/id/eprint/30394

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