Windows, accounting for 45% of overall heat loss, significantly influence a building’s energy performance. Evaluating window energy performance in existing buildings is therefore essential. This study aimed to assess the energy performance of windows to determine whether replacement is necessary. The assessment considered factors such as window location, height, orientation, and weather conditions in a high-rise residential building. A thermal camera was utilized for measurements, and statistical analyses were conducted on the collected data. Results indicated significant performance variations across building sides and floors, with differences at the 0.05 significance level attributed to temperature, wind speed, and direction. Findings suggest that implementing more efficient windows on upper floors—particularly from the fourth floor upward—is advantageous. Additionally, accounting for dominant wind speed and direction is crucial for optimizing window design and configuration.

This paper presents a comprehensive review of methodologies for building energy analysis, simulation, and optimization, with a focus on improving energy efficiency and sustainability. It synthesizes research across various domains, including data-driven models, building performance simulations (BPS), infrared thermography (IRT), and innovative design approaches. The study underscores the significance of accurate energy prediction models and the impact of early-stage design interventions on long-term energy performance. Advanced technologies such as machine learning, artificial intelligence, and statistical analysis are examined for their role in enhancing energy assessments. A key highlight of this review is the critical role of IRT as a non-invasive diagnostic tool for detecting thermal anomalies, insulation defects, and energy inefficiencies. The integration of AI with IRT is discussed as a promising advancement for automating defect detection and improving building diagnostics. Additionally, the study explores emerging materials, including high-performance insulation and phase-change materials, which contribute to sustainable construction practices. Furthermore, the paper evaluates the role of smart building technologies, parametric design, and occupancy-driven energy management in optimizing energy use. The effectiveness of various energy forecasting models, including white-box, black-box, and grey-box approaches, is analyzed, demonstrating the strengths and limitations of each methodology. Despite significant progress in the field, research gaps remain in model calibration, data integration, and real-time energy monitoring. The study proposes future directions, such as enhancing hybrid modeling techniques, standardizing assessment methods, and leveraging big data analytics to refine energy performance predictions. This review serves as a valuable resource for researchers, policymakers, and industry professionals, bridging the gap between theoretical research and practical applications in sustainable building design and energy management.

Drought has crucial impacts on socioeconomic sectors. Unlike other natural hazards (e.g., flood), drought has a creepy nature (initiates, propagates, and terminates gradually). Drought has significant impacts on socioeconomic sectors, yet its gradual and prolonged nature makes its effects challenging to quantify, particularly on financial markets. Consequently, its impacts on the stock market have been poorly quantified. This study aimed to analyze the relationship between drought severity, calculated using the Drought Severity and Coverage Index (DSCI), and stock performance in key sectors, including agriculture, water management, industrial manufacturing, and food services. Data for drought and stock market in the U.S. were obtained from the U.S. drought Monitor (USDM) and Nasdaq, respectively. Using Pearson correlation coefficients, we examined the relationship across monthly, quarterly, and yearly periods, considering no lag and other different lag times up to five years to account for delayed effects. The results revealed weak to strong correlations between drought severity and stock indices, varying with drought intensity and sector-specific characteristics. In particular, the agriculture and water management sectors showed strong negative correlations, with peak impacts observed after a 3-year lag. In contrast, industrial and food service sectors displayed weaker correlations due to their global operations and diversified supply chains. The weak correlation can be attributed to the impacts of the COVID-19 pandemic, as drought trends generally align with stock market performance, but this relationship was disrupted in 2019. These findings fill a critical gap in understanding the economic consequences of drought on financial markets and offer valuable information for forecasting stock market trends and helping businesses, investors, and policymakers better understand and address the financial risks associated with droughts.