In order to fulfil the UK government’s ambitious goal of 80% reductions in greenhouse gas emissions by 2050 compared to the levels of 1990s, unprecedented measures for improving the energy efficiency of buildings are needed. This study investigates the impact of a specific type of Low-emissivity (Low-E) window film—Thinsulate Climate Control 75—on the holistic energy consumption of an existing United Kingdom (UK) hotel building. Building modelling and energy simulation software EDSL TAS is used to conduct the study. The result of the simulations demonstrates that by applying Thinsulate films, savings in heating, cooling, and total energy consumptions are achieved by 3%, 20%, and 2.7%, respectively. Also 4.1% and 5.1% savings are achieved in annual CO₂ emissions and total energy costs, respectively, while the initial costs may be an issue. This study found that application of Low-E window films results in slightly better energy performance of the hotel regarding its heating-dominant climate. The study also recommends using average annual actual energy consumption data for a time range, instead of picking a single year’s data for validating purposes.

Residential buildings contribute 30% of the UK’s total final energy consumption. However, with less than one percent of its housing stock being replaced annually, retrofitting existing homes has significant importance in meeting energy-efficiency targets. Consequently, many physics-based and data-driven models and tools have been developed to analyse the effects of retrofit strategies from various points of view. This paper aims to develop a data-driven AI model that predicts buildings’ energy performance based on their features under various retrofit scenarios. In this context, four different machine learning models were developed based on the EPC (Energy Performance Certificate) dataset for residential buildings and standard assessment procedure (SAP) guidelines in the UK. Additionally, an interface was designed that enables users to analyse the effect of different retrofit strategies on a building’s energy performance using the developed AI models. The results of this study revealed the artificial neural network as the most accurate predictive model, with a coefficient of determination (R²) of 0.82 and a mean percentage error of 11.9 percent. However, some conceptual irregularities were observed across all the models when dealing with different retrofit scenarios. All summary, such tools can be further improved to offer a potential alternative or support to physics-based models, enhancing the efficiency of retrofitting processes in buildings.

Enclosed-space ventilation plays a pivotal role in enhancing indoor air quality (IAQ). Various regulations and guidelines determine minimum fresh air requirements, varying by the application, country, etc. The present study employs real-time indoor CO₂ measurements to calculate the actual air change rate (ACH) and models the building’s compliance with Chartered Institution of Building Services Engineers (CIBSE) guidelines to assess its impact on IAQ. The contaminant considered is the SARS-CoV-2 virus with multiple infectors, and the study calculates the virus’s transmission risk under different scenarios. Fresh air is assumed to be provided by mechanical or hybrid ventilation. The results reveal that the current building conditions are unacceptable, showing a significant gap from the recommended guideline rates. Meeting CIBSE recommendations ensures indoor air safety in terms of CO₂ concentration. However, in extreme conditions with five infectors, findings show a 14% reduction in mechanical ventilation efficacy. Among the hybrid ventilation scenarios, combining CIBSE rates with natural ventilation, with a 30% door and window opening from the class outset, proves most effective in reducing the virus’s transmission risk, with a 58–70% reduction from the building’s current condition. Furthermore, implementing supplementary measures like air cleaners resulted in an additional 28–50% reduction in transmission risk.

In light of the recent launch of the Minimum Energy Efficiency Standard and its expected impact on the commercial buildings sector, this study investigated the impact of adding cooling systems on the annual energy consumption, carbon dioxide emissions and energy performance certificate (EPC) rating of an existing UK hotel. Thermal Analysis Software (TAS) was used to conduct the study, and the baseline model was validated against the actual data. As is the current accepted procedure in EPC generating in the UK, the cooling set points of the guest rooms were set to 25 °C, resulting in a small increase in the annual energy consumption and emission rates, but not enough to change the energy performance certificate rating. Also, it was found that an improvement in energy consumption and energy performance certificate rating of the hotel would be achieved if the new systems replaced the existing heating systems in the guest rooms. Further simulations investigated more realistic situations, in which occupants may decide to keep their rooms at cooler temperatures. The results from this round of simulations showed considerable increase in the energy consumption and emissions of the building; however, these results would not be considered in the current approved procedure for EPC generating.

In light of the recent launch of the Minimum Energy Efficiency Standard targeting the energy performance of commercial buildings, this study compares the energy performance certificates of three UK hotels generated by two different software, EDSL TAS and SBEM, both accredited by the UK government for the purpose. Upon finding the results discrepant, the study finds that the two software’s different assumptions for the air permeability rate contribute to the discrepancy. While modifying this value makes the results from the two software more aligned, further issues regarding the validation process arise. The study continues to find that the underlying issue can be found within the National Calculation Methodology’s assumption about domestic hot water consumption in hotels. These assumptions are compulsory to follow when generating a non-domestic energy performance certificate in the UK, therefore, any uncertainties within them can affect all the buildings seeking an energy performance certificate within that sector. Finally, the study discusses that, for meeting the carbon dioxide mitigation goals, it is necessary to make changes to the current procedure of energy performance certificate generating in the UK to increase its reliability.

Accurate prediction of building energy performance is critical for achieving sustainability goals and reducing operational costs. This study presents a novel and automated simulation framework that integrates EnergyPlus 25.1 with modular JSON configurations and Python 3.11 scripting to streamline the modelling and analysis process. Using the Hilton Watford Hotel in the UK as a case study, the framework generates detailed Input Data Files (IDFs) based on architectural and operational data, enabling efficient exploration of various usage scenarios through batch simulations. Automation is achieved using custom Python scripts built on the Eppy library, allowing scalable modification and generation of simulation inputs. Post-processing and visualisation are performed using Pandas 2.0.3, NumPy 1.25.2, and Matplotlib 3.7.2, while model outputs are calibrated against measured performance data in accordance with ASHRAE guidelines. To enhance predictive capabilities, machine learning algorithms—Random Forest and XGBoost—are applied to estimate annual energy consumption under different operating conditions. This integrated approach not only reduces manual modelling effort but also narrows the gap between predicted and actual performance, offering a replicable pathway for retrofitting analysis and energy policy support in similar commercial buildings.