Current building energy modeling (BEM) tools lack the capability to inherently simulate the impacts of urban microclimates on building energy performance. While efforts have been made to integrate BEM with Urban Microclimate Modeling (UMM) tools, their ability to capture spatial and seasonal microclimate variations remains limited. This review critically evaluates existing urban microclimate-integrated BEM approaches and their effectiveness in modeling the complex interactions between urban form, microclimate, and building energy performance. Through an analysis of 94 research articles, the review first examines the influence of urban form on microclimates, followed by an assessment of how microclimatic conditions impact building energy use. Additionally, it evaluates conventional modeling frameworks employed in BEM tools and their limitations in representing dynamic microclimatic variations. The findings emphasize the non-linear heat exchange relationships between urban form and microclimate, typically modeled using computationally intensive Computational Fluid Dynamics (CFD)-based UMM tools. This review introduces a classification of heat exchange types: atmospheric heat exchange, involving air temperature, wind, and humidity, and non-atmospheric heat exchange, driven by radiative interactions with surrounding urban surfaces. The study further highlights that modifying standard weather files and heat transfer coefficients alone is insufficient for BEM tools to accurately capture near-surface microclimate variations. By identifying critical insights and research gaps, this review establishes a foundation for advancing next-generation urban microclimate-integrated BEM approaches, emphasizing the need for computationally efficient and dynamically responsive modeling techniques.

India’s fossil-fuel-based energy dependency is up to 68%, with the commercial and residential sectors contributing to the rise of building energy demand, energy use, and greenhouse gas emissions. Several studies have shown that the increasing building energy demand is associated with increased space-cooling ownership and building footprint. The energy demand is predicted to grow further with the conditions of global warming and the phenomenon of urban heat islands. Building designers have been using state-of-the-art transient simulation tools to evaluate energy-efficient envelopes with present-day weather files that are generated with historical weather datasets for any specific location. Designing buildings with historical climatic conditions makes the buildings vulnerable to the predicted climate change impacts. In this paper, a weather file generator was developed to generate Indian future weather files using a geo-filtering-based spatial technique, as well as the temporal downscaling and machine learning (ML)-based bias correction approach proposed by Belcher et al. The future weather files of the three representative concentration pathways of 2.6, 4.5, and 8.5 could be generated for the years 2030, 2050, 2070, 2090, and 2100. Currently, the outputs of the second-generation Canadian Earth System Model are being used to create future weather files that will aid architects, urban designers, and planners in developing a built environment that is resilient to climate change. The novelty lies in using observed historical data from present-day weather files on the typical meteorological year for testing and training ML models. The typical meteorological weather files are composed of the concatenation of the monthly weather datasets from different years, which are referred to for testing and training ML models for bias correction.

Representative climate days are specific days that reflect typical weather conditions for a given location. Representative climate days are essential for reducing the computational demands of urban building energy and microclimate models, which typically require annual simulations. Traditionally, to identify representative climate days from typical meteorological year (TMY) weather files, unsupervised Machine Learning (ML) methods are employed. Traditional ML methods, though effective for high-dimensional data, struggle with bimodal weather distributions, where two distinct climatic regimes occur within the same period. This study introduces a novel hybrid ML framework for accurately clustering bimodal weather data through a multiphase unsupervised clustering process.The framework starts by applying principal component analysis (PCA) to TMY data for reducing dimensionality. Next, misclustered days—identified using the silhouette score—undergo iterative re-clustering using k-Means and Gaussian Mixture Models (GMM) until fewer than 30 remain. Finally, representative climate days are determined from the properly clustered groups using a medoid-based weighted sampling approach. The potential of this hybrid framework is demonstrated using TMY data of the Tel Aviv, comprising of bimodal distribution. The k-Means, GMM, and hierarchical agglomerative clustering achieved higher silhouette scores through multiphase clustering over traditional approach. While k-Means-based multiphase clustering achieved the highest silhouette score, GMM demonstrated superior clustering performance by preserving month-to-month continuity, which is crucial for capturing seasonal variations. By maintaining seasonal continuity in representative days, this approach enhances the reliability of climate-based urban performance simulations, supporting more accurate and computationally efficient modelling.

Developing countries such as Iran are rapidly expanding, putting pressure on non-renewable energy resources. The building sector takes a major share of the total energy consumption of the country and is projected to increase further, resulting in the call for strategies to reduce energy use by improving the thermal performance of buildings. This study addresses the compelling need to provide optimum design guidelines for future apartment buildings in the city of Shiraz by investigating two urban cluster typologies, stair and pyramid, arranged in five orientations. The results showcase the ideal combination of 155° for the Pyramid typology, which contributes the least to the annual energy loads of the buildings.

Building Energy Modelling (BEM) and Urban Microclimate Modelling (UMM) tools support informed decision-making in built environment design. However, running large-scale or coupled simulations poses significant computational challenges. Running these simulations using representative climate days is a viable alternative, but identifying these days from Typical Meteorological Year (TMY) weather files typically requires machine learning expertise. To overcome this technical barrier, the Representative Climate Days Recognizer (ReCliDaR) tool was developed with three distinct operational modes. For graphical interface users, it offers a web application and a Windows standalone executable. For researchers with basic coding skills, a Python research engine with examples is provided to enable batch processing of multiple TMY weather files. This three-mode operation allows ReCliDaR to support built environment stakeholders in reducing computational loads while making informed design decisions.