In the last three decades, the human population has rapidly increased and continues to do so leading to a severe impact on the requirement of energy needs. Nanotechnology is a technology of opportunity that offers a wide variety of resources to address the issues associated with energy as smaller than 100 nm components and appliances present new opportunities for energy capture, storage, and exchange. The high energy demand has started a quest for alternative renewable sources of energy, especially solar energy can satisfy the growing power demands without exhausting conventional resources. Different types of nanomaterial are used to harness solar energy to meet the required needs and solar irradiance (SIR) is a crucial component of solar applications. The precise forecasting of SIR helps in the efficient management of solar energy systems and is of prime importance task. In this study, different technology that harnesses SIR with the help of nanomaterials has been discussed and further, a deep learning (DL) based model is designed to forecast the amount of solar energy that will be produced at a given site. This study uses NASA’s solar irradiation historical data (from the date 1/1/2010 to 31/12/2021) of India’s North Central Region (NCR). The experiment results depict the higher performance of the proposed hybrid model as compared to the ML (machine learning), LSTM and GRU individually on different parameters such as MSE (mean squared error), RMSE (root mean squared error), MAE (mean absolute error) and R².

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