Predictive optimization using long short-term memory for solar PV and EV integration in relatively cold climate energy systems with a regional case study.

The global shift toward sustainable energy and electric mobility addresses environmental concerns related to fossil fuels. While these alternatives are increasingly utilized in residential and commercial sectors, integrating renewable energy in building systems presents significant challenges. This is particularly evident in cold regions where unpredictable resource availability complicates energy reliability. The study emphasizes the need for innovative approaches to address these complexities and ensure consistent energy performance in dynamic conditions. This research explores the energy dynamics within a residential community located in a relatively cold climate region (Tabriz). The study begins by estimating the energy requirements of individual buildings, including the additional demand generated by electric vehicles. It then evaluates the potential for solar energy generation from photovoltaic systems. Finally, a machine learning-based approach (i.e., LSTM, Long Short-Term Memory) is employed to optimize the management of energy supply and demand across the community. This study demonstrates that heating demands in a cold climate are substantially higher than cooling needs, with solar energy providing sufficient (~ 32.1%) coverage during warmer months but requiring grid support in colder seasons. The prediction of EV charging patterns using LSTM models achieved over 93% accuracy, enabling improved energy demand forecasting and load management. These findings highlight the potential for optimizing renewable energy use, reducing grid dependency, and enhancing energy efficiency through effective production-demand management.