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Global solar radiation (GSR) is an essential parameter for the design and operation of solar energy systems. Long-standing records of global solar radiation data are not available in many places because of the cost and maintenance of the measuring instruments. The major objective of this work is to develop an ANN model for accurately predicting solar radiation. Two ANN models with four different algorithms are considered in the present study. Meteorological data collected for the last 10 years from five different locations across India have been used to train the models. The best ANN algorithm and model are identified based on minimum mean absolute error (MAE) and root mean square error (RMSE) and maximum linear correlation coefficient (R). Further, the present study confirms that prediction accuracy of the ANN model depends on the complete set of data being used for training the network for the intended application. The developed ANN model has a low mean absolute percentage error (MAPE) which ascertains the accuracy and suitability of the model to predict the monthly average global radiation so as to design or evaluate solar energy installations, where the meteorological data measuring facilities are not in place in India. All Rights Reserved

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The heat transfer enhancement of paraffin wax, a cheap and widely used latent heat thermal energy storage material, using nanoparticles is investigated. The effects of nanoparticle volume fraction on both the melting and solidification rates of paraffin wax are analysed and compared for Al 2 O 3 and CuO nanoparticles. Present results show that dispersing nanoparticles in smaller volumetric fractions increase the heat transfer rate. The enhancement in thermal performance of paraffin wax is greater for Al 2 O 3 compared with that for CuO nanoparticles. .