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Smart city article details

Title	Deep Learning And Multi-Horizontal Solar Energy Forecasting Of Different Weather Conditions In Smart Cities
ID_Doc	17813
Authors	Sharma P.K.; Kumar M.V.K.; Ahmad M.W.; Radhika M.
Year	2024
Published	Sustainable Smart Homes and Buildings with Internet of Things
DOI	http://dx.doi.org/10.1002/9781394231522.ch12
Abstract	Variations in the power system's total voltage and regularity are directly caused by increased renewable energy pumped into the power grid. Therefore, it is critical to have accurate predictions of renewable energy sources for reliable power dispatch and grid operations. Three models using three sets of meteorological data are compared in this article to see which one produces the most accurate one-day photovoltaic (PV) power prediction. This research proposes a preprocessing data framework to address the issue of weather data loss, quantity, and matching, all of which impact the model training outcomes. The models used include an artificial neural network (ANN), a long short-term memory (LSTM), and a gated recurrent unit (GRU), all based on deep learning algorithms. The weather data is categorized into three sets: Central Weather Bureau (CWB), local web server (LWS), and hybrid data, which is a mix of the two. Measurements in the hybrid group improved by 5–8% associated to the other two collections. The benefits of the LSTM model in action across various weather scenarios. Additional research confirmed, via one month's worth of forecasts, that the LSTM model using hybrid data formed the most exact readings. Lastly, according to the findings, training the model using hybrid data and the five elements’ variables is influential when data is restricted. Consequently, the suggested model demonstrates improved PV forecasting for the next day. © 2025 Scrivener Publishing LLC. All rights reserved.
Author Keywords	deep learning; farmwork; hybrid data; LSTM; Power prediction

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