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.

scholarly journals Evaluation of Urban-Scale Building Energy-Use Models and Tools—Application for the City of Fribourg, Switzerland

2021 ◽  
Vol 13 (4) ◽  
pp. 1595
Author(s):  
Valeria Todeschi ◽  
Roberto Boghetti ◽  
Jérôme H. Kämpf ◽  
Guglielmina Mutani
Keyword(s):  
Machine Learning ◽  
Energy Use ◽  
Residential Buildings ◽  
Building Energy ◽  
Energy Performance ◽  
Space Heating ◽  
Engineering Model ◽  
Building Energy Use ◽  
The Impact ◽  
The City

Building energy-use models and tools can simulate and represent the distribution of energy consumption of buildings located in an urban area. The aim of these models is to simulate the energy performance of buildings at multiple temporal and spatial scales, taking into account both the building shape and the surrounding urban context. This paper investigates existing models by simulating the hourly space heating consumption of residential buildings in an urban environment. Existing bottom-up urban-energy models were applied to the city of Fribourg in order to evaluate the accuracy and flexibility of energy simulations. Two common energy-use models—a machine learning model and a GIS-based engineering model—were compared and evaluated against anonymized monitoring data. The study shows that the simulations were quite precise with an annual mean absolute percentage error of 12.8 and 19.3% for the machine learning and the GIS-based engineering model, respectively, on residential buildings built in different periods of construction. Moreover, a sensitivity analysis using the Morris method was carried out on the GIS-based engineering model in order to assess the impact of input variables on space heating consumption and to identify possible optimization opportunities of the existing model.

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.

Evaluation of the Impact of Window Shading on the Anti-Insulation Phenomenon in Building Energy Use

MRS Advances ◽  
2018 ◽  
Vol 3 (34-35) ◽  
pp. 2063-2073
Author(s):  
R. K. Rabasoma ◽  
D. D. Serame ◽  
O.T. Masoso
Keyword(s):  
Solar Radiation ◽  
Energy Use ◽  
Common Knowledge ◽  
Building Design ◽  
Building Energy ◽  
Climatic Regions ◽  
Building Energy Use ◽  
The World ◽  
Building Design Decisions ◽  
The Impact

ABSTRACTBefore 2008, it was common knowledge around the world that insulation always saved air conditioning energy in buildings. In 2008 a phenomenon called anti-insulation was brought to light by Masoso & Grobler. They demonstrated that there are instances when insulation materials in a building directly increase building energy use. Researchers around the world then echoed the message. Recent work by some of the authors investigated the anti-insulation phenomenon in summer and winter for both hot climatic regions (Botswana) and cold climatic regions (Canada). Their study concluded that there is still a mystery of exaggerated sources of heat inside the building aggravating the anti-insulation phenomenon. They hypothesized that incident solar radiation through the windows could be one of the causes. This paper therefore focuses on eliminating direct solar radiation through windows by applying external shadings on a previously anti-insulation building. The energy saved is evaluated and the possible reversal of anti-insulation studied. The study is useful to energy policy makers and the building industry as it showcases the existence of a possible silent killer (anti-insulation) and demonstrates that investing large sums of money on insulation in buildings may not be the most economic thing to do in building design decisions.

scholarly journals The Impact of Trees on Building Energy Use

2019 ◽  
Author(s):  
Ulrike Passe ◽  
Jan Thompson ◽  
Baskar Ganapathysubramanian ◽  
Boshun Gao ◽  
Breanna Marmur ◽  
...  
Keyword(s):  
Energy Use ◽  
Building Energy ◽  
Building Energy Use ◽  
The Impact

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 Outdoor Thermal Comfort and Building Energy Use Potential in Different Land-Use Areas in Tropical Cities: Case of Kuala Lumpur

Atmosphere ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 652
Author(s):  
Yasemin D. Aktas ◽  
Kai Wang ◽  
Yu Zhou ◽  
Murnira Othman ◽  
Jenny Stocker ◽  
...  
Keyword(s):  
Land Use ◽  
Thermal Comfort ◽  
Energy Use ◽  
Building Energy ◽  
Outdoor Thermal Comfort ◽  
Kuala Lumpur ◽  
Air Temperatures ◽  
Building Energy Use ◽  
Urban Heat ◽  
The Impact

High air temperature and high humidity, combined with low wind speeds, are common trends in the tropical urban climates, which collectively govern heat-induced health risks and outdoor thermal comfort under the given hygrothermal conditions. The impact of different urban land-uses on air temperatures is well-documented by many studies focusing on the urban heat island phenomenon; however, an integrated study of air temperature and humidity, i.e., the human-perceived temperatures, in different land-use areas is essential to understand the impact of hot and humid tropical urban climates on the thermal comfort of urban dwellers for an appraisal of potential health risks and the associated building energy use potential. In this study, we show through near-surface monitoring how these factors vary in distinct land-use areas of Kuala Lumpur city, characterized by different morphological features (high-rise vs. low-rise; compact vs. open), level of anthropogenic heating and evapotranspiration (built-up vs. green areas), and building materials (concrete buildings vs. traditional Malay homes in timber) based on the calculated heat index (HI), apparent temperature (TApp) and equivalent temperature (TE) values in wet and dry seasons. The results show that the felt-like temperatures are almost always higher than the air temperatures in all land-use areas, and this difference is highest in daytime temperatures in green areas during the dry season, by up to about 8 °C (HI)/5 °C (TApp). The TE values are also up to 9% higher in these areas than in built-up areas. We conclude that tackling urban heat island without compromising thermal comfort levels, hence encouraging energy use reduction in buildings to cope with outdoor conditions requires a careful management of humidity levels, as well as a careful selection of building morphology and materials.

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