Evaluation of School Building Energy Performance and Classroom Indoor Environment

Theoretical study of the synergic relationships between the design parameters in energy-saving building design

With the rapid development of the economy, people have increasingly higher requirements for the comfort of living spaces, and the result is the sharp increase in building energy consumption. Several design parameters influence living space comfort and building energy efficiency. Since the same design standard can include different design parameter combinations, synergic relationships may exist between these criteria for one case. Identifying these synergic relationships requires an inverse problem approach. This paper established a model by combining an improved genetic algorithm (IGA) and numerical calculation to determine the synergic design parameter relationships (e.g. the thermophysical building material properties and energy-saving factors). For [Formula: see text], the shading coefficient significantly influenced the linear function between the thermal conductivity and insulation thickness. In this case, the insulation thickness was exponentially related to the shading coefficient, while the thermal conductivity of the insulation material significantly impacted the synergic relationship. For [Formula: see text], the insulation thickness was a segmented function of the shading coefficient. The results verified that the proposed model was efficient and reliable for identifying the synergic relationships between energy-saving parameters. In engineering applications, designers can select the optimal design parameter combination based on the relationship curve between the parameters in this paper according to the local market conditions and specific design requirements.

Comparative analysis on the effectiveness of green roofs and photovoltaic panels as sustainable rooftop technologies

Photovoltaic (PV) panels and green roofs are considered as the most effective sustainable rooftop technologies at present, which utilizes the effective rooftop area of a building in a sustainable manner. To assess the most suitable rooftop technology out of the two, it is vital to have an idea on the energy savings potential of these sustainable rooftop technologies, alongside a financial feasibility analysis considering their overall life spans and additional ecosystem services. To achieve this objective, ten selected rooftops located in a tropical city were retrofitted with hypothetical PV panels and semi-intensive green roof scenarios to perform the present analysis. The energy-saving potential for PV panels was estimated with the assistance of PVsyst software, and green roof ecosystem services were evaluated through a range of empirical formulas. The financial feasibility of the two technologies was assessed by Payback Period and Net Present Value (NPV), through data obtained by local information sources such as solar panels and green roof manufacturers. The results indicate that PV panels achieve a rooftop PV potential of 244.39 KWh/yr/m2 during their 20-year life span. Furthermore, green roofs reach an energy-saving potential of 22.29 KWh/yr/m2 during a 50-year life span. Moreover, based on the financial feasibility analysis, PV panels demonstrated an average payback period of 3-4 years. Green roofs exemplified 17-18 years to recover their total investment for the selected case studies in Colombo, Sri Lanka. Although green roofs do not provide comparatively significant energy savings, these sustainable rooftop technologies aid in energy saving under different response intensities. In addition, green roofs offer several other ecosystem services that improve urban areas' quality of life. Collectively, these findings highlight the particular importance of each rooftop technology promoting building energy savings.

Building Energy Audits—Diagnosis and Retrofitting towards Decarbonization and Sustainable Cities

A collection of twelve papers published in Energies, in a Special Issue on “Building Energy Audits-Diagnosis and Retrofitting”, was bound together and published in 2021, focusing on the built environment. The aim was to systematically collect and analyze relevant data for obtaining adequate knowledge on the energy use profile of buildings, and was extended for the sustainability assessment of the built environment. To date, all papers have been very well received, attaining a total of 97 citations and over 15,300 views. The papers addressed historic and various building types, baselines for non-residential buildings from energy performance audits and from in-situ measurements, monitoring and data analysis, assessment of indoor environmental quality, model calibration and verification of energy savings, along with an urban audit and rating method for assessing the sustainability of the built environment. Following on from the success of this Special Issue, the decision was made to reopen and extend it to include papers related to decarbonization and sustainability, at building, city, region, and national scales. This Editorial reviews the performance of the first Special Issue and outlines the second volume on Building Energy Audits-Diagnosis and Retrofitting Towards Decarbonization and Sustainable Cities, as a Special Issue in Energies.

A Procedure for Automating Energy Analyses in the BIM Context Exploiting Artificial Neural Networks and Transfer Learning Technique

One of the main benefits of Building Information Modelling is the capability of improving the decision-making process thanks performing what-if tests on digital twins of the building to be realized. Pairing BIM models to Building Energy Models allows designers to determine in advance the energy consumption of the building, improving sustainability of the construction. The challenge is to consider as many elements involved in the energy balance as possible and shuffling their parameters within a certain range. In this work, the automatic creation of a relevant set of design options to be analyzed for searching the optimum has been carried out. Firstly, the usual workflow that would be applied manually has been automatically followed by running scripts and codes, depending just on the initial setup given by the user. Although the procedure is very resource consuming, the main advancement relies in the reduction of the manual intervention and the possibility of creating large datasets of design options, avoiding gross errors. Secondly, Artificial Neural Networks and Transfer Learning techniques are applied to speed up the process of dataset creation. With such approach, the same dataset has been created, with about 30% of initial data and without significant loss of accuracy.

Comment on Pietrapertosa et al. How to Prioritize Energy Efficiency Intervention in Municipal Public Buildings to Decrease CO2 Emissions? A Case Study from Italy. Int. J. Environ. Res. Public Health 2020, 17, 4434

This paper responds to the article by Pietrapertosa et al., doi:10.3390/ijerph17124434, published previously in the International Journal of Environmental Research and Public Health. Its aim is to discuss the appropriateness of the studied method, to analyze its weak sides and to propose its robustness improvement. Thus, data presented in the above study were examined and recalculated, yielding, among others, indicators of annual energy savings (in kWh per m² of total heated area) and specific proposals investment costs (in € per m² of total heated area). By analyzing the obtained data for all public buildings, a significantly simplified approach to this problematic has been suggested while several other features of the research method and some presented results lack proper reasoning and discussion. Individual approach to each public building has been proposed and discussed point-by-point to enhance the method’s applicability. As a result, more realistic outcomes are obtained, and suitable investment actions can be proposed.

Winter Thermal Comfort and Perceived Air Quality: A Case Study of Primary Schools in Severe Cold Regions in China

In Northeast China, most classrooms in primary and secondary schools still use natural ventilation during cold days in winter. This study investigated the thermal comfort and the perceived air quality of children in primary schools in severe cold regions in China. Field measurements were conducted in four typical primary classrooms in two naturally ventilated teaching buildings in the winter of 2016 in the provincial city of Shenyang. Six field surveys were distributed to 141 primary students aged 8 to 11, and 835 valid questionnaires were collected. The results showed that the indoor temperature and the daily mean CO2 concentrations of the primary school classrooms ranged from 17.06 to 24.29 °C and from 1701 to 3959 ppm, respectively. The thermal neutral temperature of the primary school students was 18.5 °C, and the 90% thermal comfort temperature ranged from 17.3 to 20.1 °C. Children were able to respond to changes in indoor air quality, but there was no significant correlation between the children’s perceptions of air quality and the carbon dioxide levels in the classroom. In general, children have a lower comfort temperature than adults. In addition, children are more sensitive to temperature changes during the heating season than adults. Due to differences in thermal sensation between children and adults, the current thermal comfort standard based on adult data is not applicable to primary school buildings and children. The air quality evaluation during heating season indicates that it is necessary to add indoor air environment monitoring instruments and purification equipment to the naturally ventilated classrooms. At present and in the future, more research based on children’s data is needed to solve the indoor air environment problems in primary school buildings.