Predicting U.S. Residential Building Energy Use and Indoor Pollutant Exposures in the Mid-21st Century

2021 ◽  
Vol 55 (5) ◽  
pp. 3219-3228
Author(s):  
Torkan Fazli ◽  
Xinyi Dong ◽  
Joshua S. Fu ◽  
Brent Stephens
Keyword(s):  
21St Century ◽  
Energy Use ◽  
Residential Building ◽  
Building Energy ◽  
Building Energy Use ◽  
Indoor Pollutant

scholarly journals Impacts of COVID-19 on residential building energy use and performance

2021 ◽  
pp. 108200
Author(s):  
Emily Kawka ◽  
Kristen Cetin
Keyword(s):  
Energy Use ◽  
Residential Building ◽  
Building Energy ◽  
Building Energy Use ◽  
And Performance

Data-driven predictive models for residential building energy use based on the segregation of heating and cooling days

Energy ◽  
2020 ◽  
Vol 206 ◽  
pp. 118045 ◽  
Author(s):  
Ehsan Kamel ◽  
Shaya Sheikh ◽  
Xueqing Huang
Keyword(s):  
Predictive Models ◽  
Energy Use ◽  
Residential Building ◽  
Building Energy ◽  
Data Driven ◽  
Heating And Cooling ◽  
Building Energy Use

A Comparative Study on Building Energy Performance between Movable and Fixed Shading Materials

2012 ◽  
Vol 164 ◽  
pp. 85-88
Author(s):  
Xiao Chang Yang ◽  
Jian Yao
Keyword(s):  
Energy Use ◽  
Residential Building ◽  
Building Energy ◽  
Energy Performance ◽  
Simulation Software ◽  
Public Building ◽  
Building Energy Performance ◽  
Energy Demands ◽  
Building Energy Use ◽  
Better Than

The purpose of this paper is to investigate the effect of movable shading on building energy demands. A public building and a residential building with movable shading and fixed shading were modeled by the building energy use simulation software DOE-2. Cooling and heating energy demands were calculated. The results showed that movable shading is better than fixed shading.

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.

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