Charging electric vehicles (EVs) and integrating renewable energy sources (RESs) are becoming key aspects of residential energy systems. However, the variability of RES generation, combined with uncontrolled EV charging, poses challenges for reliability, power quality, and supply-demand balancing within communities. The challenges only grow when considering other electrified building loads as well. Accurate forecasting of power demand and renewable generation is essential for efficient and sustainable grid operation, optimal use of RESs, and effective energy trading within communities. Deep learning (DL), including supervised, unsupervised, and reinforcement learning (RL), has emerged as a promising solution for predicting consumer demand, renewable generation, and managing energy flows in residential environments. This paper provides a comprehensive review of the development and application of these methods for forecasting and energy management in residential communities. Evaluation metrics across studies indicate that supervised learning can achieve highly accurate forecasting results, especially when integrated with unsupervised K-means clustering and data decomposition. These methods help uncover patterns and relationships within the data while reducing noise, thereby enhancing prediction accuracy. RL shows significant potential in control applications, particularly for charging strategies. Similarly to how V2G-simulators model individual EV usage and simulate large fleets to generate grid-scale predictions, RL can be applied to various aspects of EV fleet management, including vehicle dispatching, smart scheduling, and charging coordination. Traditional methods are also used across different applications and help utilities with planning. However, these methods have limitations and may not always be completely accurate. Our review suggests that integrating hybrid supervised-unsupervised learning methods with RL can significantly improve the sustainability and resilience of energy systems. This approach can improve demand and generation forecasting while enabling smart charging coordination and scheduling for scalable EV fleets integrated with building electrification measures. Furthermore, the review introduces a unifying conceptual framework that links forecasting, optimization, and policy coupling through hierarchical deep learning layers, enabling scalable coordination of EV charging, renewable generation, and building energy management. Despite methodological advances, real-world deployment of hybrid and deep learning frameworks remains constrained by data-privacy restrictions, interoperability issues, and computational demands, highlighting the need for explainable, privacy-preserving, and standardized modeling approaches. To be effective in practice, these methods require robust data acquisition, optimized forecasting and control models, and integrated consideration of transport, building, and grid domains. Furthermore, deployment must account for data privacy regulations, cybersecurity safeguards, model interpretability, and economic feasibility to ensure resilient, scalable, and socially acceptable solutions.

This review systematically synthesizes technological synergies within a Community Energy System (CES), emphasizing cold-climate contexts where heating-dominant demand profiles and strong seasonality create distinct operational challenges. Drawing on 115 studies (2010–2024), the paper explores how integrated thermal, electrical, and digital infrastructures support net-zero and climate-resilient communities in regions with substantial heating requirements. Thermal–electrical coupling emerges as a foundational mechanism in cold climates, where heating loads dominate annual energy demand and drive winter peak constraints. Power-to-Heat (P2H) systems, cold-climate heat pumps, and hybrid configurations combining Thermal Energy Storage (TES) with Battery Energy Storage Systems (BESS) enable multi-timescale flexibility, allowing renewable energy to be shifted from hours to seasons. District Energy Systems (DES) act as a thermal backbone, enabling this integration across extended heating seasons and transforming thermal demand into a grid-balancing resource. Digital technologies further enhance system coordination under variable climatic conditions. Artificial Intelligence (AI), the Internet of Things (IoT), and Advanced Metering Infrastructure (AMI) support real-time optimization, demand response, and cross-vector control within Renewable Energy Communities (RECs) and Virtual Power Plants (VPPs). At the system level, decentralized architectures—including microgrids, Non-Wire Alternatives (NWAs), and peer-to-peer (P2P) trading—strengthen resilience by maintaining thermal and electrical continuity during grid disruptions. Building on these findings, the review synthesizes cross-cutting technological synergies and proposes deployment pathways tailored to cold-climate CES, supported by comparative case studies. Despite demonstrated benefits, widespread adoption remains constrained by high upfront costs, interoperability challenges, and fragmented regulatory frameworks. The review concludes with policy, governance, and research recommendations to enable scalable, equitable, and climate-responsive CES deployment in heating-dominated regions.