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Smart city article details

Title	Solar Power Forecasting In Smart Cities Using Deep Learning Approaches: A Review
ID_Doc	52235
Authors	Sankari S.S.; Kumar P.S.
Year	2024
Published	International Research Journal of Multidisciplinary Technovation, 6, 6
DOI	http://dx.doi.org/10.54392/irjmt24610
Abstract	Solar power forecasting is important in smart cities to balance the energy demand with the energy supply. As solar energy is an inexhaustible clean energy source, it can provide sustainability and bulk energy generation economically. The rapid transition of urban cities into smart cities is increasing power demand in many countries. Solar power is a dominant renewable energy source for the success of smart cities. Solar power generation is purely depends on the photovoltaic (PV) panels and sunlight. Hence, the solar panels can also be installed easily on the rooftop. The reliable power is guaranteed by installing solar panels on rooftop in smart cities. The dependability of smart city functions relies on a steady power supply, making accurate solar power forecasting essential. The paper focuses on exploring the research work done in solar power forecasting. It discusses the functioning of smart cities, describes the importance of solar power for the efficient functioning of smart cities, addresses the challenges of solar power forecasting, and presents the applications of deep learning methodologies such as recurrent neural network (RNN), long short-term memory (LSTM), gated recurrent unit (GRU) and hybrid models in solar power forecasting. © The Author(s) 2024.
Author Keywords	Electricity demand; Energy forecasting; Photovoltaic panels; Renewable energy; Sustainable Energy

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