The comprehensive approach for a building envelope design involves building performance simulations, which are time-consuming and require knowledge of complicated processes. In addition, climate variation makes the selection of these parameters more complex. The paper aims to establish guidelines for determining a single-family household’s unique optimal passive design in various climate zones worldwide. For this purpose, a bi-objective optimization is performed for twenty-four locations in twenty climates by coupling TRNSYS and a non-dominated sorting genetic algorithm (NSGA-III) using the Python program. The optimization process generates Pareto fronts of thermal load and investment cost to identify the optimum design options for the insulation level of the envelope, window aperture for passive cooling, window-to-wall ratio (WWR), shading fraction, radiation-based shading control, and building orientation. The goal is to find a feasible trade-off between thermal energy demand and the cost of thermal insulation. This is achieved using multi-criteria decision making (MCDM) through criteria importance using intercriteria correlation (CRITIC) and the technique for order preference by similarity to ideal solution (TOPSIS). The results demonstrate that an optimal envelope design remarkably improves the thermal load compared to the base case of previous envelope design practices. However, the weather conditions strongly influence the design parameters. The research findings set a benchmark for energy-efficient household envelopes in the investigated climates. The optimal solution sets also provide a criterion for selecting the ranges of envelope design parameters according to the space heating and cooling demands of the climate zone.

Rising temperatures, increase in population, and dense urban morphology have resulted in increased cooling energy demands. The conventional degree-days method to calculate cooling energy demand considers only the sensible heat load of air and neglects the latent component. This study aims to estimate the cooling degree days based on the heat index (by considering both the sensible and latent loads) for the current and future years (2050 and 2080). Further, the ventilation load index for each of these cities has been established to unlock the impact of ventilation on the building’s total energy consumption for current and future years. The results show that heat index-based degree days have a stronger relationship with the buildings’ cooling energy consumption and, therefore, can predict the cooling energy demand of buildings with 20% higher accuracy than conventional temperature-based degree days. Analysis shows that cooling degree-days and frequency of temperature above the comfort range continue to increase in Pakistan, highlighting increased degree-days in the range from 11.0 to 41.6% by 2050 and from 28.4 to 126.5% by 2080. Prompt actions are essential to enhance the resilience of Pakistan’s national grid to meet these future cooling energy demands.

It is essential to balance energy supply and demand in residential buildings through accurate forecasting of energy use due to varying daily and seasonal residential building loads. This study demonstrates a data-driven Virtual Energy Replication Framework (VERF) to predict the behavior of residential buildings using weather forecast data. The framework integrates supervised machine learning models and time-ahead weather parameters to estimate photovoltaic (PV) power production, heat pump energy consumption, and indoor thermal comfort. The accuracy of prediction models is validated using TRNSYS simulations of a typical household in Saarbrucken, Germany, a temperate oceanic climate region. The XGBoost model exhibits the highest reliability, achieving a root mean square error (RMSE) of 0.003 kW for PV power generation and 0.025 kW for heat pump energy use, with R² scores of 0.94 and 0.87, respectively. XGBoost and random forest regression models perform well in predicting PV generation and HP electricity load, with mean prediction errors of 5.27–6% and 0–7.7%, respectively. In addition, the thermal comfort index (PPD) is predicted with an RMSE of 1.84 kW and an R² score of 0.80 using the XGBoost model. The mean prediction error remains between 2.4% (XGBoost regression) and −11.5% (lasso regression) throughout the forecasted data. Because the framework requires no real-time instrumentation or detailed energy modelling, it is scalable and adaptable for smart building energy systems, and has particular value for Building-Integrated Photovoltaics (BIPV) demonstration projects on account of its predictive load-matching capabilities. The research findings justify the applicability of VERF for efficient and sustainable energy management using weather-informed prediction models in residential buildings.

Condensers are an integral part of air conditioning systems. The thermal efficiency of condensers solely depends on the rate of heat transfer from the cooling medium. Fin tubes are extensively used for heat transfer applications due to their enhanced heat transfer capabilities. Fins provide appreciable drainage because surface tension produces pressure gradients. Much research, contributed by several scientists, has focused on adjusting parameters, such as fin design, flow rates and retention angles. In this study, a setup with an observing hole was used to inspect the influence on retention angle of adjusting the flow rates of the fluid. The increase in retention angle was examined using several velocities and concentration mixtures. Pin-fin tubes were used to obtain coherent results using a photographic method. The experimental setup was designed to monitor the movement of fluid through the apparatus. The velocity was varied using dampers and visibility was enhanced using dyes. Photographs were taken at 20 m/s velocities after every 20 s. and 0.1% concentration and the flooding point observed. The experimental results were verified by standard observation which showed little variation at lower velocity. For water/water-propanol mixtures, a vapor velocity of 12 m/s and concentration ratio of 0.04% was the optimal combination to achieve useful improvement in retention angle. With increase of propanol from 0% to 0.04%, the increase in retention angle was greater compared to 0.04% to 0.1%. For velocities ranging from 0 to 12 m/s, the increase in retention angle was significant. A sharp change was observed for concentration ratios ranging from 0.01% to 0.05% compared to 0.05% to 0.1%.

In Pakistan and most other developing nations, the residential building sector is one of the highest energy-consuming domains. The residential sector has the highest share of 50% of final electricity use of the country. Though Energy Conservation Building Codes (ECBC-2023) provide structured energy guidelines, no work has been performed to quantify the actual energy-saving potential of code-compliant retrofits in residential buildings. This study investigates the performance of ECBC-compliant retrofitting strategies for residential buildings under Pakistan’s diverse climatic conditions. The Passive House Planning Package (PHPP), a validated simulation tool, was used to assess energy performance improvements through building envelope interventions such as thermal insulation, solar shading, window glazing, and optimal orientation. Field data were collected from three representative cities, Multan (hot desert), Taxila (humid subtropical), and Quetta (cold semi-arid), to simulate both conventional and energy-efficient building scenarios. The results showed substantial seasonal energy savings in all three climates. During the heating period, energy savings were 48%, 50%, and 60% for Taxila, Multan, and Quetta, respectively. Similarly, energy savings during the cooling season were 44%, 33%, and 16%. Life cycle economic analysis revealed that these retrofits yielded Net Present Values (NPVs) of USD 752 (Taxila), USD 1226 (Multan), and USD 1670 (Quetta) over a 30-year period, with discounted payback periods ranging from 6 to 10 years. Furthermore, a life cycle assessment demonstrated that retrofitted buildings yielded up to 26% reduction in overall carbon emissions, combining both embodied and operational sources. The findings highlight that ECBC-2023 is not only a technically viable solution for energy savings but also financially attractive in residential retrofitting. By incorporating localized climate responsiveness into ECBC-compliant building design, the study provides a practical roadmap for achieving Pakistan’s energy efficiency goals. Additionally, the outcomes serve as a basis for informing policy initiatives, supporting building code adaptation, and raising public awareness of sustainable housing practices.