Impact of climate change on building energy use in different climate zones and mitigation and adaptation implications

2012 ◽  
Vol 97 ◽  
pp. 274-282 ◽  
Author(s):  
Kevin K.W. Wan ◽  
Danny H.W. Li ◽  
Wenyan Pan ◽  
Joseph C. Lam
Keyword(s):  
Climate Change ◽  
Energy Use ◽  
Building Energy ◽  
Climate Zones ◽  
Impact Of Climate Change ◽  
Mitigation And Adaptation ◽  
Building Energy Use

scholarly journals Long-Term Prediction of Weather for Analysis of Residential Building Energy Consumption in Australia

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4805
Author(s):  
Shu Chen ◽  
Zhengen Ren ◽  
Zhi Tang ◽  
Xianrong Zhuo
Keyword(s):  
Climate Change ◽  
Energy Consumption ◽  
Residential Building ◽  
Building Energy ◽  
Space Heating ◽  
Building Energy Consumption ◽  
Climate Zones ◽  
Impact Of Climate Change ◽  
Heating And Cooling ◽  
The Impact

Globally, buildings account for nearly 40% of the total primary energy consumption and are responsible for 20% of the total greenhouse gas emissions. Energy consumption in buildings is increasing with the increasing world population and improving standards of living. Current global warming conditions will inevitably impact building energy consumption. To address this issue, this report conducted a comprehensive study of the impact of climate change on residential building energy consumption. Using the methodology of morphing, the weather files were constructed based on the typical meteorological year (TMY) data and predicted data generated from eight typical global climate models (GCMs) for three representative concentration pathways (RCP2.6, RCP4.5, and RCP8.5) from 2020 to 2100. It was found that the most severe situation would occur in scenario RCP8.5, where the increase in temperature will reach 4.5 °C in eastern Australia from 2080–2099, which is 1 °C higher than that in other climate zones. With the construction of predicted weather files in 83 climate zones all across Australia, ten climate zones (cities)—ranging from heating-dominated to cooling-dominated regions—were selected as representative climate zones to illustrate the impact of climate change on heating and cooling energy consumption. The quantitative change in the energy requirements for space heating and cooling, along with the star rating, was simulated for two representative detached houses using the AccuRate software. It could be concluded that the RCP scenarios significantly affect the energy loads, which is consistent with changes in the ambient temperature. The heating load decreases for all climate zones, while the cooling load increases. Most regions in Australia will increase their energy consumption due to rising temperatures; however, the energy requirements of Adelaide and Perth would not change significantly, where the space heating and cooling loads are balanced due to decreasing heating and increasing cooling costs in most scenarios. The energy load in bigger houses will change more than that in smaller houses. Furthermore, Brisbane is the most sensitive region in terms of relative space energy changes, and Townsville appears to be the most sensitive area in terms of star rating change in this study. The impact of climate change on space building energy consumption in different climate zones should be considered in future design strategies due to the decades-long lifespans of Australian residential houses.

scholarly journals Application of Machine Learning for Predicting Building Energy Use at Different Temporal and Spatial Resolution under Climate Change in USA

Buildings ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. 139 ◽  
Author(s):  
Rezvan Mohammadiziazi ◽  
Melissa M. Bilec
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Energy Use ◽  
Regression Tree ◽  
Building Energy ◽  
Gradient Boosting ◽  
Change Models ◽  
Building Energy Use ◽  
Extreme Gradient Boosting ◽  
Single Regression

Given the urgency of climate change, development of fast and reliable methods is essential to understand urban building energy use in the sector that accounts for 40% of total energy use in USA. Although machine learning (ML) methods may offer promise and are less difficult to develop, discrepancy in methods, results, and recommendations have emerged that requires attention. Existing research also shows inconsistencies related to integrating climate change models into energy modeling. To address these challenges, four models: random forest (RF), extreme gradient boosting (XGBoost), single regression tree, and multiple linear regression (MLR), were developed using the Commercial Building Energy Consumption Survey dataset to predict energy use intensity (EUI) under projected heating and cooling degree days by the Intergovernmental Panel on Climate Change (IPCC) across the USA during the 21st century. The RF model provided better performance and reduced the mean absolute error by 4%, 11%, and 12% compared to XGBoost, single regression tree, and MLR, respectively. Moreover, using the RF model for climate change analysis showed that office buildings’ EUI will increase between 8.9% to 63.1% compared to 2012 baseline for different geographic regions between 2030 and 2080. One region is projected to experience an EUI reduction of almost 1.5%. Finally, good data enhance the predicting ability of ML therefore, comprehensive regional building datasets are crucial to assess counteraction of building energy use in the face of climate change at finer spatial scale.

Building Energy Use and Climate Change

2021 ◽  
pp. 1-7
Author(s):  
Negin Imani ◽  
Brenda Vale
Keyword(s):  
Climate Change ◽  
Energy Use ◽  
Building Energy ◽  
Building Energy Use

scholarly journals Impact Assessment in the Process of Propagating Climate Change Uncertainties into Building Energy Use

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 367
Author(s):  
Hamed Yassaghi ◽  
Simi Hoque
Keyword(s):  
Climate Change ◽  
Regression Model ◽  
Energy Use ◽  
Uncertainty Propagation ◽  
Principal Component Regression ◽  
Principal Component ◽  
Building Energy ◽  
Weather Variables ◽  
Building Energy Use ◽  
The Impact

Buildings are subject to significant stresses due to climate change and design strategies for climate resilient buildings are rife with uncertainties which could make interpreting energy use distributions difficult and questionable. This study intends to enhance a robust and credible estimate of the uncertainties and interpretations of building energy performance under climate change. A four-step climate uncertainty propagation approach which propagates downscaled future weather file uncertainties into building energy use is examined. The four-step approach integrates dynamic building simulation, fitting a distribution to average annual weather variables, regression model (between average annual weather variables and energy use) and random sampling. The impact of fitting different distributions to the weather variable (such as Normal, Beta, Weibull, etc.) and regression models (Multiple Linear and Principal Component Regression) of the uncertainty propagation method on cooling and heating energy use distribution for a sample reference office building is evaluated. Results show selecting a full principal component regression model following a best-fit distribution for each principal component of the weather variables can reduce the variation of the output energy distribution compared to simulated data. The results offer a way of understanding compound building energy use distributions and parsing the uncertain nature of climate projections.

Climate change: Projections and implications to building energy use

2019 ◽  
Vol 12 (4) ◽  
pp. 585-596 ◽  
Author(s):  
Zhiqiang John Zhai ◽  
Jacob Michael Helman
Keyword(s):  
Climate Change ◽  
Energy Use ◽  
Building Energy ◽  
Climate Change Projections ◽  
Building Energy Use
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