Influence of overheating criteria in the appraisal of building fabric performance

In response to the threat of anthropogenic climate change, heating dominated countries have focused on re-ducing the space conditioning demand by increasing insulation and airtightness. However, given climate projec-tions and lifespan of buildings, concerns have arisen on whether these strategies deliver resilient solutions. As overheating can be evaluated through different criteria, this paper investigates if building fabric performance is subject to bias from the assessment method chosen and account for discrepancies between previous studies.To answer this, we modelled dwellings compliant with 1995 and 2006 UK building regulations and the FEES and Passivhaus standards in a consistent and realistic manner. The parametric study included different weathers, thermal mass, glazing ratios, shading strategies, occupancy profiles, infiltration levels, purge ventilation strate-gies and orientations, resulting in 16128 simulation models. To provide confidence in the output, the base model was first validated against data collected from a real well-insulated dwelling.Results show that the benchmark choice is influential in the evaluation of building fabric performance as it is able to inverse overheating trends. Criteria based on adaptive comfort best represented expected behaviour, where improved building fabric is a resilient measure that reduces overheating as long as occupants are able to open windows for ventilation

Enhancing predictive models for short-term forecasting electricity consumption in smart buildings

Lighting, heating, and air conditioning systems are instances of how electricity use at buildings is of key importance for occupants comfort and well-being. Since the electricity can be produced but cannot be stored, for utility companies it is important to reliably forecast energy supply almost in near real-time. Nowadays, smart grid technologies development also require a high resolution forecasting to eliminate blackouts and to optimally adapt energy supply to customers’ needs. These are the reasons why the finest Machine Learning and Data Science based methods have been developed and applied to approach as much accurate as possible predictive models for short-term electricity consumption. This paper proposes to enhance those predictive models by using weather and calendar information to configure a more complete working database. In addition, a cluster-based forecasting methodology will augment any predictive model with learning from other buildings. Thus, predicting future values for one smart meter is approached by utilising not only its own historical electricity consumption values, but working with a multivariate time series on weather and calendar data and information from other buildings at the same cluster. This proposal has been tested with measures from smart meters collected every 30 minutes during one year for 5 selected buildings in Bristol (UK). The enhancing methodology can predict electricity consumption data with higher accuracy than using data from just one building

Dataset for "Mitigation versus adaptation: Does insulating buildings increase overheating risk?"

Dataset for journal article "Mitigation versus adaptation: Does insulating buildings increase overheating risk?". The dataset contains the summary simulation results of the building simulation parametric study (EnergyPlus v8.9) for overheating, natural ventilation and space heating demand (annual simulations with yearly indicators). The dataset contains the performance of all the buildings that combine the following parameters: dwelling types, insulation levels, thermal mass, window sizes, shading strategies, internal gains, window opening rubrics, algorithms, infiltration levels, building orientations and locations.Parametric building simulations in EnergyPlus v8.9.See `readme.txt`.See `readme.txt`.See `readme.txt`

Dataset for "Mitigation versus adaptation: Does insulating buildings increase overheating risk?"

Dataset for journal article "Mitigation versus adaptation: Does insulating buildings increase overheating risk?". The dataset contains the summary simulation results of the building simulation parametric study (EnergyPlus v8.9) for overheating, natural ventilation and space heating demand (annual simulations with yearly indicators). The dataset contains the performance of all the buildings that combine the following parameters: dwelling types, insulation levels, thermal mass, window sizes, shading strategies, internal gains, window opening rubrics, algorithms, infiltration levels, building orientations and locations.Fosas, D., Coley, D., Natarajan, S., Herrera Fernandez, M., Fosas de Pando, M., Ramallo-Gonzalez, A., 2018. Dataset for "Mitigation versus adaptation: Does insulating buildings increase overheating risk?". Bath: University of Bath Research Data Archive. Available from: https://doi.org/10.15125/BATH-00390

Dataset for "Improving Thermal Comfort in Refugee Shelters in Desert Environments"

The dataset describes monitored environmental conditions of unoccupied shelter prototypes in the refugee camp of Azraq (Jordan). The monitored environmental conditions are temperature and relative humidity every hour both outdoors and indoors. The 12 shelter prototypes include a control shelter without modifications and 11 variants implementing a range of passive/active measures (increased ventilation, insulation, thermal mass and/or roof shades, evaporative cooler and earth tube).The work was completed using two hypotheses: 1. It is possible to take an existing shelter design, that is already deployed at scale and has been shown via field surveys to provide unsatisfactory thermal comfort, and complete a parametric analysis using field testing of prototypes to generate an affordable derivative of the same appearance that provides improved internal thermal conditions as measured against acceptable thermal comfort bands. 2. That similar results can be achieved using thermal modelling without any field testing even though many of the modelling inputs are unlikely to be accurately known. To assess our hypotheses, the thermal conditions within eleven variants of identical form but representing different possible improvement strategies were compared to a base design. The success of the prototypes was assessed by comparing the maximum daytime temperature and number of degree hours over comfort levels compared to the control shelter. The success of computational modelling was to be assessed by comparing predicted performance against monitored performance. Twelve unoccupied test shelters were monitored (temperature, humidity, opening of doors and windows) for ten weeks from 15/7/19 – 21/9/19. Nine shelters received passive adaptations, two received active adaptations, all adaptations were focused on improving internal summer thermal comfort. The shelters were simulated in EnergyPlus v9.0.1 Two metrics are used to evaluate the thermal performance of each shelter: air temperature and operative temperature. Overheating is typically quantified as the total number of hours the threshold has been surpassed (total hour count, OH_ch [h]). However, such a metric fails to account for the severity of overheating, since deviations of 1 K over the threshold are more benign than deviations of 6 K. For this purpose, a second metric weighs (multiplies) the duration of overheating according to the temperature deviation above the threshold (to give weighted hours of overheating, OH_wh [K·h]). Here we use the ASHRAE Standard 55 Thermal Comfort Model together with that found in Vellei. The change in internal conditions provided by the interventions are assessed for both the physical and modelled shelters using the following metrics: 1. Minimum air temperature difference between indoors and outdoors, rTp-e (°C). Negative numbers indicate the extent to which air temperature in the shelter is cooler than the external air temperature. 2. Maximum indoor air temperature difference between shelter variant and control shelter, rTp-c (°C). Negative numbers indicate the extent to which the shelter is cooler than the control shelter. 3. Minimum indoor air temperature, T_min (°C) 4. Mean indoor air temperature, T_avg (°C) 5. Maximum indoor air temperature, T_max (°C) 6. T_comf_max_ashrae: ASHRAE upper limit for indoor operative temperature (80% acceptability) (°C) 7. T_comf_max_vellei: Vellei’s model upper limit for indoor operative temperature (80% acceptability) (°C) 8. Total overheating hours compared with the adaptive comfort temperature, OH_ch (h). The metric is provided for the two thresholds considered, ASHRAE’s and Vellei’s. 9. Total overheating Kelvin hours compared with the adaptive comfort upper temperature, OH_wh (K·h). The metric is provided for the two thresholds considered, ASHRAE’s and Vellei’s

Dataset for "Mitigation versus adaptation: Does insulating buildings increase overheating risk?"

Dataset for journal article "Mitigation versus adaptation: Does insulating buildings increase overheating risk?". The dataset contains the summary simulation results of the building simulation parametric study (EnergyPlus v8.9) for overheating, natural ventilation and space heating demand (annual simulations with yearly indicators). The dataset contains the performance of all the buildings that combine the following parameters: dwelling types, insulation levels, thermal mass, window sizes, shading strategies, internal gains, window opening rubrics, algorithms, infiltration levels, building orientations and locations

Dataset for "Mitigation versus adaptation: Does insulating buildings increase overheating risk?"

Dataset for journal article "Mitigation versus adaptation: Does insulating buildings increase overheating risk?". The dataset contains the summary simulation results of the building simulation parametric study (EnergyPlus v8.9) for overheating, natural ventilation and space heating demand (annual simulations with yearly indicators). The dataset contains the performance of all the buildings that combine the following parameters: dwelling types, insulation levels, thermal mass, window sizes, shading strategies, internal gains, window opening rubrics, algorithms, infiltration levels, building orientations and locations