Leveraging Information Systems, Big Data Analytics, and AI for Energy-Efficient Design of Rural Residences

The integration of Information Systems (IS), Big Data Analytics (BDA), and Artificial Intelligence (AI) has ushered in a new era of energy-efficient design for rural residences. This study delves into the intricate synergy between technology and sustainability, unveiling the transformative potential of these tools in reshaping rural living spaces. The exploration spans from the conceptualization of designs to their real-world implementation, highlighting the pivotal role of IS in facilitating collaborative efforts among stakeholders. The study further uncovers the power of Big Data Analytics in deciphering energy consumption patterns, climatic variations, and occupant behaviours. These insights lay the groundwork for AI-powered simulations that optimize energy efficiency while ensuring occupant comfort. The study underscores the consequences of ineffective design, elucidating how it amplifies energy consumption, escalates environmental impact, and compromises residents' quality of life. In contrast, the integration of IS, BDA, and AI results in energy-efficient residences, marked by reduced energy usage, enhanced indoor comfort, and economic savings. Despite challenges such as limited resources, harsh climates, and technical expertise gaps, innovative solutions in the form of training programs, data privacy protocols, and collaborations emerge as beacons of progress. Looking to the future, emerging trends like smart grids, Internet of Things (IoT) integration, and AI-driven predictive maintenance shape the narrative of rural residences design. Rural communities stand poised for self-sufficiency and sustainability, empowered by the fusion of technology and ecological mindfulness. The recommendations presented in this study offer actionable insights for construction professionals, policymakers, and researchers, emphasizing interdisciplinary collaboration, continuous monitoring, and ongoing training. Future directions include greater investigation of new trends in sustainability, smart grids, and predictive maintenance, which will help rural communities become self-sufficient and environmentally conscientious.