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Title	Electricity Consumption Forecasting For Sustainable Smart Cities Using Machine Learning Methods
ID_Doc	22572
Authors	Peteleaza D.; Matei A.; Sorostinean R.; Gellert A.; Fiore U.; Zamfirescu B.-C.; Palmieri F.
Year	2024
Published	Internet of Things (Netherlands), 27
DOI	http://dx.doi.org/10.1016/j.iot.2024.101322
Abstract	Integrating smart grids in smart cities is pivotal for enhancing urban sustainability and efficiency. Smart grids enable bidirectional communication between consumers and utilities, enabling real-time monitoring and management of electricity flows. This integration yields benefits such as improved energy efficiency, incorporation of renewable sources, and informed decision-making for city planners. At the city scale, forecasting electricity consumption is crucial for effective resource planning and infrastructure development. This study proposes using a time-series dense encoder model for short-term and long-term forecasting at the city level, showing its superior performance compared to traditional approaches like recurrent neural networks and statistical methods. Hyperparameters are optimized using the non-dominated sorting genetic algorithm. The model's efficacy is demonstrated on a six-year dataset, highlighting its potential to significantly improve electricity consumption forecasting and enhance urban energy system efficiency. © 2024 The Author(s)
Author Keywords	Electricity consumption forecasting; Machine learning; Smart city; Sustainability; Time-series dense encoder

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