Abstract
Abstract. This study focuses on the assessment of surface solar radiation (SSR) based on operational neural network (NN) and multi-regression function (MRF) modelling techniques that produce instantaneous (in less than 1 min) outputs. Using real-time cloud and aerosol optical properties inputs from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board the Meteosat Second Generation (MSG) satellite and the Copernicus Atmosphere Monitoring Service (CAMS), respectively, these models are capable of calculating SSR in high resolution (1 nm, 0.05∘, 15 min) that can be used for spectrally integrated irradiance maps, databases and various applications related to energy exploitation. The real-time models are validated against ground-based measurements of the Baseline Surface Radiation Network (BSRN) in a temporal range varying from 15 min to monthly means, while a sensitivity analysis of the cloud and aerosol effects on SSR is performed to ensure reliability under different sky and climatological conditions. The simulated outputs, compared to their common training dataset created by the radiative transfer model (RTM) libRadtran, showed median error values in the range −15 to 15 % for the NN that produces spectral irradiances (NNS), 5–6 % underestimation for the integrated NN and close to zero errors for the MRF technique. The verification against BSRN revealed that the real-time calculation uncertainty ranges from −100 to 40 and −20 to 20 W m−2, for the 15 min and monthly mean global horizontal irradiance (GHI) averages, respectively, while the accuracy of the input parameters, in terms of aerosol and cloud optical thickness (AOD and COT), and their impact on GHI, was of the order of 10 % as compared to the ground-based measurements. The proposed system aims to be utilized through studies and real-time applications which are related to solar energy production planning and use.
Highlights
Solar energy exploitation is a cornerstone for sustainable development, through efficient energy planning, towards the goal of gradual independence from fossil fuels
We report on (i) the assessment of the surface solar irradiance calculated in real time, which is defined as the product with a time delay of 1 min or less from an actual atmospheric situation, by developing and using two neural network (NN)-based techniques and a multi-regression-function-based technique and (ii) the validation of these techniques against ground-based measurements from the Baseline Surface Radiation Network (BSRN)
This study proposed state-of-the-art modelling techniques (NNS, NN, multi-regression function (MRF)) for the real-time estimation of surface solar radiation (SSR), which have been validated against ground-based BSRN measurements
Summary
Solar energy exploitation is a cornerstone for sustainable development, through efficient energy planning, towards the goal of gradual independence from fossil fuels. In this direction, the European Union (EU), the Middle East and North Africa (MENA) and numerous neighbouring regions and countries have laid out specific technology roadmaps aiming at the integration of low carbon energy technologies linked with the deployment of photovoltaic (PV) installations in the energy market (IPCC, 2012; NREL, 2016; IRENA, 2016; Jager-Waldau, 2016; REN21, 2017; UN, 2017). The United Nations (2017) has set as its main sustainable development goal by 2030 to ensure universal access to afford-. Transportation and consumption put considerable pressure on the environment, there is serious concern regarding the sustainability of energy consumption
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