Electricity consumption forecasting for sustainable smart cities using machine learning methods

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.