On the Impact of Climate Change on Building Energy Consumptions: A Meta-Analysis

Energy use and its contributors in hotel buildings: A systematic review and meta-analysis

Energy use is the major source of carbon emissions in hotel buildings. Past studies presented contributors to energy use, most related to hotels' physical and economics characteristics. In search of effective variables affecting energy use in hotels, this systematic review and meta-analysis aims to synthesize empirical evidence. A descriptive picture of 28 previous studies, the arguments for the direction of effects in each variable, and a quantitative synthesis of the mean effect sizes were presented. Among 18 selected contributors from past studies, 15 were statistically significant (0.05 level). The analyses also revealed that the operationalization of the energy variable is important in evaluating the relationship with a contributor. Studies considering Energy Use Intensity (EUI) indicators presented weaker correlations with gross floor area (GFA) and number of guestrooms than those considering energy raw data, for example. The occupancy rate resulted in a non-significant outcome, but this result could be related to differences among the hotels categories, as identified in the subgroup and meta-regression analyses. Future research could help develop and investigate theories to sustain or deny the relationships found here, in addition to the assessment of the outcomes in other regions, bringing more variables related to sustainable management.

The socioeconomic impact of climate change in developing countries over the next decades: A literature survey

Extreme weather events, rising temperatures, and shifting rainfall patterns pose significant threats to developing countries with fragile social, economic, and political structures. While research has intensified on socioeconomic impacts of climate change, existing survey studies exhibit substantial scope variations and seldom concurrently analyze these impacts, hindering policy coordination. This study reviews literature on the broad spectrum of socioeconomic impacts of climate change to discern trends and underscore areas requiring additional attention. The survey unveils that, across various socioeconomic indicators, the most vulnerable groups bear a disproportionate burden of climate change, with long-term impacts forecasted to surpass medium-term effects. Adaptation and mitigation options are feasible but must be tailored to local contexts.

BIM and ANN-based rapid prediction approach for natural daylighting inside library spaces

The issue of how to fully utilize natural daylighting of public buildings is one of the greatest practical objectives for lighting savings. The rapid and accurate prediction of the daylighting coefficient at the early design stage can provide a quantitative basis for energy-saving optimization. However, it is not comprehensive to determine the design parameters according to experience. The key problem that is still facing designers is the interoperability between building modeling and energy simulation tools. In this paper, an integrated approach using a dataset created by building information modeling and artificial neural network technology is developed for the fast optimal daylight factor prediction of large public spaces at the early design stage. According to this approach, the value of daylight factors is calculated for different windowsill heights, window heights and widths by Autodesk ® Revit and Ecotect Analysis to form a dataset. With this dataset, an artificial neural network model is established using the backpropagation algorithm to predict the relevant design parameters. With their large interior spaces, the reading areas of the aboveground five floors in Chengdu University of Technology Library are selected to carry out the daylight factor experiment and rapid prediction. A total of 495 groups of experimental data are randomly divided into training and testing sets. The root mean squared errors are below 0.1, which indicates a high regression model fitting. A total of 225,369 groups of prepared data are used in the prediction model to obtain the optimal windowsill height (1.0 m), window height (2.4 m) and window width (2.1 m) for five floors in the case of the maximum daylighting coefficient. Finally, a smartphone app is designed to facilitate daylight factor prediction without any experience in modeling and simulation tools, which is simple and available to realize prediction visualization and historical result analysis.

Analyzing Electricity Consumption Factors of Buildings in Seoul, Korea Using Multiscale Geographically Weighted Regression

The recent increase in energy consumption worldwide has accelerated global warming. Thus, developed countries are aiming to reduce energy consumption in cities and promote eco-friendly policies. Buildings account for most of the energy used in a city. Therefore, it is necessary to identify the factors that affect electrical energy consumption in urban buildings. In this study, we use multiscale geographically weighted regression (MGWR) to analyze these urban characteristic factors at the global and local scales in Seoul, Korea. It is found that population and household characteristics, outdoor temperature, green and water areas, building area according to building usage, and construction age significantly affect the electrical energy consumption of buildings. In addition, the influences of these variables change with the region. Variables with different coefficients by region are winter temperature, green and water area, and households with three or more persons. The results confirm that even within a city, the influence of the aforementioned factors varies in terms of spatial distribution and patterns. This study is significant as it carried out basic research for energy consumption reduction in buildings by deriving related influencing factors.

Development of Simplified Building Energy Prediction Model to Support Policymaking in South Korea—Case Study for Office Buildings

This study aims to support building energy policymaking for office buildings in South Korea through regression models by considering the global temperature rise. The key variables representing building energy standards and codes are selected, and their impact on the annual energy consumption is simulated using EnergyPlus reference models. Then, simplified regression models are built on the basis of the annual energy consumption using the selected variables. The prediction performance of the developed model for forecasting the annual energy consumption of each reference building is good, and the prediction error is negligible. An additional global coefficient is estimated to address the impact of increased outdoor air temperature in the future. The final model shows fair prediction performance with global coefficients of 1.27 and 0.9 for cooling and heating, respectively. It is expected that the proposed simplified model can be leveraged by non-expert policymakers to predict building energy consumption and corresponding greenhouse gas emissions for the target year.

Climate Change Impact on Energy Poverty and Energy Efficiency in the Public Housing Building Stock of Bari, Italy

The public housing stock across the European Union is generally constituted of old buildings (built prior to 1980) with high energy demand and indoor thermal comfort issues, which could be exacerbated by climate change. The aim of this paper was to quantify the impact of climate change on the energy demand of the public housing building stock. A neighbourhood located in Bari (south Italy) is considered as representative of a common construction typology of late 1970s in Italy. Energy models were created and calibrated with real-time data collected from utilities’ bills. The results showed a medium to strong correlation between age and energy consumption (r = 0.358), but no evident correlation between the number of tenants and energy consumption, although a significantly low energy consumption was found in apartments occupied by more than five tenants. An energy penalty of about 7 kWh/m2 of heating energy consumption for every 10 years of increase in the average age of tenants was calculated. Moreover, the impact of future weather scenarios on energy consumptions was analysed and an average annual energy penalty of 0.3 kWh/m2 was found.

Construction Solutions and Materials to Optimize the Energy Performances of EPS-RC Precast Bearing Walls

The design and employment of envelope components showing high thermal performances for new buildings and deep renovations must take into account the overall impact of the production process in terms of environmental sustainability. To this end, precast construction solutions and secondary raw materials provide added value to the energy quality of building products. With regard to the abovementioned issues, the paper is focused on the performance optimization of expanded polystyrene-reinforced concrete (EPS-RC) precast bearing walls, already developed and patented within a previous research project entitled “HPWalls. High Performance Wall Systems”, and herein improved according to two complementary requirements: on the one hand, the addition of recycled EPS particles to the concrete mixtures and, thus, the assessment by lab tests of the correlation between the thermal and mechanical properties for several mix-design specimens; on the other hand, a study using analytical simulations of the most suitable joint solutions among modular panels in order to prevent thermal bridges. The achieved results validate the proposed optimization strategies and provide reliable data for market applications in the building sector.

Performance Comparison of Different Building Shapes Using a Wind Tunnel and a Computational Model

A building-integrated wind turbine (BIWT) is an alternative way to assess renewable energy. BIWTs produces their own energy without relying on fossil fuels. However, only a few researchers have studied BIWTs. Greater wind velocity (V) results in greater potential energy (P). The aerodynamic design has an important role to play in increasing wind velocity and reducing turbulence intensity. CFD simulations taken from previous research have revealed that round-shaped buildings increase velocity up to 30%. This study focuses on the wind response of square and top-rounded-shaped building models, and their optimization based on variations in wind velocity. Wind tunnel studies were conducted to study wind flow around the building, followed by a computer simulation to verify the results. In a wind tunnel, three BIWT models (1:150 in scale) located in Seoul, South Korea (terrain B), were evaluated. The results of the study show that the streamline should be followed when installing wind turbines on rectangular rooves with flat surfaces. This method allows wind speed to be elevated significantly, when compared to a turbine at a higher height. In addition, round corners can produce wind velocity that is up to 34% greater than sharp corners beside a building. In summary, this paper presents a five-step analysis framework that can be used by researchers who wish to analyze BIWTs through wind tunnel experiments and CFD.

The Impact of Climate Change on a University Campus’ Energy Use: Use of Machine Learning and Building Characteristics

Global warming is expected to increase 1.5 °C between 2030 and 2052. This may lead to an increase in building energy consumption. With the changing climate, university campuses need to prepare to mitigate risks with building energy forecasting models. Although many scholars have developed buildings energy models (BEMs), only a few have focused on the interpretation of the meaning of BEM, including climate change and its impacts. Additionally, despite several review papers on BEMs, there is no comprehensive guideline indicating which variables are appropriate to use to explain building energy consumption. This study developed building energy prediction models by using statistical analysis: multivariate regression models, multiple linear regression (MLR) models, and relative importance analysis. The outputs are electricity (ELC) and steam (STM) consumption. The independent variables used as inputs are building characteristics, temporal variables, and meteorological variables. Results showed that categorizing the campus buildings by building type is critical, and the equipment power density is the most important factor for ELC consumption, while the heating degree is the most critical factor for STM consumption. The laboratory building type is the most STM-consumed building type, so it needs to be monitored closely. The prediction models give an insight into which building factors remain essential and applicable to campus building policy and campus action plans. Increasing STM is to raise awareness of the severity of climate change through future weather scenarios.