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

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

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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