Propagating downscaled future weather file uncertainties into building energy use

Yassaghi, Hamed; Gurian, Patrick L.; Hoque, Shamia

doi:10.1016/j.apenergy.2020.115655

Cited by 18 publications

(11 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This study assesses the impact of various average annual weather variables distributions and selecting different regression model in building energy use distributions when conducting an uncertainty propagation analysis. We follow the four-step uncertainty propagation technique presented by Yassaghi, Gurian and Hoque (2020) [33] to conduct our impact assessment analysis. The four-step climate uncertainty propagation method consists of a development of regression model, fitting distribution to weather variables and random sampling ( Figure 1).…”

Section: Methodology 21 Study Approachmentioning

confidence: 99%

“…The four-step climate uncertainty propagation method consists of a development of regression model, fitting distribution to weather variables and random sampling ( Figure 1). The Four-Step Uncertainty Propagation method followed in the impact assessment (adapted from method proposed by [33]).…”

Section: Methodology 21 Study Approachmentioning

confidence: 99%

“…Finally, by having a regression model (between the average annual weather variables and energy use) and input average annual weather variables distribution, the uncertainties can be propagated to the energy use distribution. In the uncertainty propagation method presented by [33] the use of different regression models or input average annual weather variable distributions could alter the output energy use distribution results.…”

Section: Methodology 21 Study Approachmentioning

confidence: 99%

“…The process of conducting a Monte Carlo analysis can be complex and would require intense computational needs and recent studies are attempting to reduce the intensity of the uncertainty analysis. Yassaghi, Gurian and Hoque (2020) presented a four-step climate uncertainty propagation method which could reduce computational efforts and is applicable to regions where limited hourly weather data can be developed to evaluate climate change impacts on buildings energy performance [33]. However, probabilities are not always easy to assess.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Yassaghi

Hoque

2021

Energies

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Methodology 21 Study Approachmentioning

confidence: 99%

Section: Methodology 21 Study Approachmentioning

confidence: 99%

Section: Methodology 21 Study Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Yassaghi

Hoque

2021

Energies

Self Cite

View full text Add to dashboard Cite

show abstract

“…There are many examples of studies that have explored the uncertainty impacts of climate data [16][17][18][19][20][21][22], user behavior [23][24][25][26][27], envelope and material properties [28][29][30], and HVAC systems [31,32] on building energy use. Uncertainty analysis in evaluating building energy performance can be conducted following forward approaches (e.g., Monte Carlo (MC)), which propagate input uncertainties through energy models to observe energy use variations, in addition to inverse approaches (e.g., Bayesian methods) [3].…”

Section: Introductionmentioning

confidence: 99%

Partitioning Climate, Users, and Thermophysical Uncertainties from Building Energy Use: A Monte Carlo & ANOVA Approach

et al. 2022

Self Cite

View full text Add to dashboard Cite

Buildings are subject to many uncertainties ranging from thermophysical performance to user activity. Climate change is an additional source of uncertainty that complicates building performance evaluation. This study aims to quantify the share of uncertainties stemming from building factors, user behavior, and climate uncertainty from boilers, chillers, fans, pumps, total HVAC systems, and total site energy use. A novel method combining Monte Carlo analysis and ANOVA is proposed to partition uncertainties from building energy simulation results under different climate change scenarios. The Monte Carlo method is used to generate distributions of building and user factors as building simulation inputs. Then, simulation results under current and future climate conditions are post-processed using a three-way ANOVA technique to discretize the uncertainties for a reference office building in Philadelphia, PA. The proposed method shows the share in percentages of each input factor (building, user, and climate) in the total uncertainty of building energy simulation output results. Our results indicate that the contribution of climate uncertainty increases from current conditions to future climate scenarios for chillers, boilers, fans, and pumps’ electricity use. User parameters are the dominant uncertainty factor for total site energy use and fans’ electricity use. Moreover, boiler and HVAC energy use are highly sensitive to the shape and range of user and building input factor distributions. We underline the importance of selecting the appropriate distribution for input factors when partitioning the uncertainties of building performance modeling.

show abstract

Forecasting Future Climate with a Neural Network Trained on Monitored Data: An Analysis of the Energy Demand of a Detached House

Festa,

De Masi,

Gigante

et al. 2024

Green Energy and Technology

View full text Add to dashboard Cite

Propagating downscaled future weather file uncertainties into building energy use

Cited by 18 publications

References 41 publications

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

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

Partitioning Climate, Users, and Thermophysical Uncertainties from Building Energy Use: A Monte Carlo & ANOVA Approach

Forecasting Future Climate with a Neural Network Trained on Monitored Data: An Analysis of the Energy Demand of a Detached House

Contact Info

Product

Resources

About