Climatic, energy retro-fit and IEQ mitigation scenario modelling of the English classroom stock model

Health and cognitive performance in UK school classrooms is dependent on building fabric performance as well as heating and ventilation system operation in maintaining Indoor Environmental Quality (IEQ), comprising thermal comfort and air quality. While archetype models can be used to simulate IEQ for different stock-wide location and construction eras, a predictive approach also necessitates the use of longitudinal scenarios. As a key component of the UK’s decarbonisation strategy, these scenarios should account for fabric retro-fit adaptations to reduce carbon emissions, and changes in operation of the building for overheating mitigation as well as changes in external climatic conditions. The IEQ of three representative classroom archetypes, representing the stock of 18,000 English schools, have been analysed for 24 pair-wise retro-fit and operational scenarios across three climatic scenarios. Retro-fitting, while effective in reducing energy demand, may risk compromising indoor air by requiring ventilation at times of the day when external conditions are least conducive to air quality and overheating. Additionally, while North facing classrooms can tackle overheating through single effective IEQ mitigation measures, South facing and 2080 climates will necessitate cumulative effects of multiple measures to be realised. Future work involves incorporating educational and construction stakeholder preferences through multi-criteria decision analysis, to derive suitable metrics

Modelling UK school performance by coupling building simulation and multi-criteria decision analysis

Meeting indoor environment quality (IEQ) standards, incorporating air quality and thermal comfort, is critical for children’s health and learning within classrooms. While building simulation provides indicative IEQ outputs, educational and construction stakeholders may require broader criteria, such as attainment, health and healthcare costs, to assess UK school building stock performance. To investigate the provision of such metrics, a Data dRiven Engine for Archetype Models of Schools (DREAMS) EnergyPlus-based stock-modelling framework was developed, modelling different classroom typologies. Dynamic IEQ simulation has demonstrated that the influence of construction era on learning performance metrics may be stronger in hotter regions, which are increasingly reliant on ventilation to counter higher temperatures and maintain IEQ. This framework includes creating retrofit scenarios to evaluate school building energy efficiency interventions and coupling with multi-criteria decision analysis (MCDA). This integration of modelled impacts with stakeholder-derived health and educational attainment weightings could provide a basis for future interventions

A Multi-Criteria decision analysis framework to determine the optimal combination of energy efficiency and indoor air quality schemes for English school classrooms

Maintaining good Indoor Environmental Quality (IEQ) in English schools in terms of overheating and air quality is important for the health and educational performance of children. Improving energy efficiency in school buildings is also a key part of UK’s carbon emissions reduction strategy. To address the trade-offs between energy efficiency and IEQ, a Multi–Criteria Decision Analysis (MCDA) framework based on an English classroom stock model was used. The aim was to determine robust optimal school building interventions across a set of criteria (including child health, educational attainment and building energy consumption) and settings (comprising different climate scenarios, construction eras, geographical regions and school geographical orientations). Each intervention was made up of the pairwise combination of an energy efficiency retrofit scheme and an IEQ improvement scheme. The MCDA framework was applied to the school building stock in England. This study shows that the framework represents a transparent approach to support decision making in determining the optimal school building intervention from different perspectives. The optimal interventions included measures that improved IEQ and resulting indoor learning environments, such as external shading, or increased albedo and internal blinds, for the particular set of interventions, criteria and stakeholders in this study. The results of the MCDA analysis were sensitive to the preferences elicited from stakeholders on the relative importance of the criteria and to the range of interventions and criteria selected for evaluation

A Multi-Criteria decision analysis framework to determine the optimal combination of energy efficiency and indoor air quality schemes for English school classrooms

Maintaining good Indoor Environmental Quality (IEQ) in English schools in terms of overheating and air quality is important for the health and educational performance of children. Improving energy efficiency in school buildings is also a key part of UK's carbon emissions reduction strategy. To address the trade-offs between energy efficiency and IEQ, a Multi–Criteria Decision Analysis (MCDA) framework based on an English classroom stock model was used. The aim was to determine robust optimal school building interventions across a set of criteria (including child health, educational attainment and building energy consumption) and settings (comprising different climate scenarios, construction eras, geographical regions and school geographical orientations). Each intervention was made up of the pairwise combination of an energy efficiency retrofit scheme and an IEQ improvement scheme. The MCDA framework was applied to the school building stock in England. This study shows that the framework represents a transparent approach to support decision making in determining the optimal school building intervention from different perspectives. The optimal interventions included measures that improved IEQ and resulting indoor learning environments, such as external shading, or increased albedo and internal blinds, for the particular set of interventions, criteria and stakeholders in this study. The results of the MCDA analysis were sensitive to the preferences elicited from stakeholders on the relative importance of the criteria and to the range of interventions and criteria selected for evaluation