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Smart city article details

Title	Advancing Smart City Energy Management: Very Short-Term Photovoltaic Power Generation Forecasting Using Multi-Scale Long Short-Term Memory Deep Learning
ID_Doc	6687
Authors	Rao B.S.; Aparna M.; Raju M.S.N.
Year	2024
Published	Biomass and Solar-Powered Sustainable Digital Cities
DOI	http://dx.doi.org/10.1002/9781394249374.ch18
Abstract	The rapid adoption of renewable energy sources, particularly photovoltaic (PV) systems, in smart city energy management has prompted the need for accurate and forecasting PV power output in the short term. Accurate predictions are essential for efficient grid integration, demand response optimization, and enhanced energy management strategies. Traditional forecasting methods often struggle to handle the complexities of PV power generation due to its intermittent and highly variable nature. In order to accurately predict very short-term PV power generation, we investigate the use of multiscale long short-term memory, a deep learning algorithm. This development has a great deal of potential to improve energy management strategies for smart cities. © 2024 Scrivener Publishing LLC.
Author Keywords	deep learning; machine learning; multi-scale long short-term memory (LSTM); Photovoltaic (PV) power generation forecasting; power grid stability; renewable energy integration; smart city energy management; smart grids; time series forecasting; very-short-term forecasting

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