The rapid growth of the global population and urbanization has led to environmental degradation, resulting in a worldwide energy crisis. In response, the quality of architecture has evolved to prioritize energy efficiency, impacting indoor human health in the process. Green buildings have emerged as a solution to this problem, aiming to improve indoor environmental quality (IEQ) and human well-being while minimizing negative environmental impacts. This comprehensive review focuses on the role of green buildings in enhancing indoor human health and energy efficiency. It examines the published research on the effects of green buildings on IEQ and occupant health, highlighting sustainable architectural practices that promote good health. The study concludes that green buildings provide healthier environments for their occupants by creating healthy indoor environments, and minimizing negative environmental impacts. The study also explores the link between sustainable architecture and health promotion, with a focus on IEQ. It presents evidence-based and biophilic design strategies that can impact treatments and health promotion in the built environment. Overall, this review emphasizes the critical role that green buildings can play in addressing the global energy crisis while promoting the health and well-being of building occupants.

This study introduces a novel framework that leverages artificial intelligence (AI), specifically deep learning and reinforcement learning, to enhance energy efficiency in architectural design. The goal is to identify architectural arrangements that maximize energy efficiency. The complexity of these models is acknowledged, and an in-depth analysis of model selection, their inherent complexity, and the hyperparameters that govern their operation is conducted. This study validates the scalability of these models by comparing them with traditional optimization techniques like genetic algorithms and simulated annealing. The proposed system exhibits superior scalability, adaptability, and computational efficiency. This research study also explores the ethical and societal implications of integrating AI with architectural design, including potential impacts on human creativity, public welfare, and personal privacy. This study acknowledges it is in its preliminary stage and identifies its potential limitations, setting the stage for future research to enhance and expand the effectiveness of the proposed methodology. The findings indicate that the model can steer the architectural field towards sustainability, with a demonstrated reduction in energy usage of up to 20%. This study also conducts a thorough analysis of the ethical implications of AI in architecture, emphasizing the balance between technological advancement and human creativity. In summary, this research study presents a groundbreaking approach to energy-efficient architectural design using AI, with promising results and wide-ranging applicability. It also thoughtfully addresses the ethical considerations and potential societal impacts of this technological integration.

Sustainable energy strategies have been a critical subject for sustainable development, especially in cities. Citizens, as an integral part of the urban environment, play a significant role in urban spaces, as does their health. An accurate understanding of citizens’ mental, social, and physical health in urban settings is required to design and plan better cities. This study aims to assess the level of alignment with health factors in Mahabad, a major medium-sized city in Iran. Previous studies indicate that the built environment can influence health dimensions. Health factors depend to a great extent on how well the environment is formed and how it is put together. This research is a descriptive, analytical, cross-sectional study that analyzes the environment’s psychological elements and physical and mental health factors of Mahabad’s citizens. According to the Cochran model, 384 questionnaires were distributed among households. For data analysis, SPSS 12 and Arc GIS software were used. The main results of this research show that five factors, “Environmental quality”, “Identity and social relationships”, and “Readability”, have the most impact on the physical and mental health of citizens (respondents). These issues are much more pronounced in the downtown neighborhoods. This study showed that urban experts can understand different levels of public health by knowing the historical, social, cultural, and economic factors and characteristics. The result will help decision makers, city authorities, designers, and urban planners to be more informed about citizens’ health and the ways to improve it.

This study investigates the incorporation of advanced heating, ventilation, and air conditioning (HVAC) systems with reinforcement learning (RL) control to enhance energy efficiency in low-energy buildings amid the extreme seasonal temperatures of Tehran. We conducted comprehensive simulation assessments using the EnergyPlus and HoneybeeGym platforms to evaluate two distinct reinforcement learning models: traditional Q-learning (Model A) and deep reinforcement learning (DRL) with neural networks (Model B). Model B consisted of a deep convolutional network architecture with 256 neurons in each hidden layer, employing rectified linear units as activation functions and the Adam optimizer at a learning rate of 0.001. The results demonstrated that the RL-managed systems resulted in a statistically significant reduction in energy-use intensity of 25 percent (p < 0.001), decreasing from 250 to 200 kWh/m² annually in comparison to the baseline scenario. The thermal comfort showed notable improvements, with the expected mean vote adjusting to 0.25, which falls within the ASHRAE Standard 55 comfort range, and the percentage of anticipated dissatisfaction reduced to 10%. Model B (DRL) demonstrated a 50 percent improvement in prediction accuracy over Model A, with a mean absolute error of 0.579366 compared to 1.140008 and a root mean square error of 0.689770 versus 1.408069. This indicates enhanced adaptability to consistent daily trends and irregular periodicities, such as weather patterns. The proposed reinforcement learning method achieved energy savings of 10–15 percent compared to both rule-based and model predictive control and approximately 10 percent improvement over rule-based control, while employing fewer building features than existing state-of-the-art control systems.