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Title: Simulated building energy demand biases resulting from the use of representative weather stations

Abstract

Numerical building models are typically forced with weather data from a limited number of “representative cities” or weather stations representing different climate regions. The use of representative weather stations reduces computational costs, but often fails to capture spatial heterogeneity in weather that may be important for simulations aimed at understanding how building stocks respond to a changing climate. Here, we quantify the potential reduction in temperature and load biases from using an increasing number of weather stations over the western U.S. Our novel approach is based on deriving temperature and load time series using incrementally more weather stations, ranging from 8 to roughly 150, to evaluate the ability to capture weather patterns across different seasons. Using 8 stations across the western U.S., one from each IECC climate zone, results in an average absolute summertime temperature bias of ~4.0 °C with respect to a high-resolution gridded dataset. The mean absolute bias drops to ~1.5 °C using all available weather stations. Temperature biases of this magnitude could translate to absolute summertime mean simulated load biases as high as 13.5%. Increasing the size of the domain over which biases are calculated reduces their magnitude as positive and negative biases may cancel out. Usingmore » 8 representative weather stations can lead to a 20–40% bias of peak building loads during both summer and winter, a significant error for capacity expansion planners who may use these types of simulations. Using weather stations close to population centers reduces both mean and peak load biases. Our approach could be used by others designing aggregate building simulations to understand the sensitivity to their choice of weather stations used to drive the models.« less

Authors:
 [1];  [1];  [1];  [1];  [1]
  1. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
OSTI Identifier:
1413471
Report Number(s):
PNNL-SA-125597
Journal ID: ISSN 0306-2619; PII: S030626191731228X
Grant/Contract Number:
AC05-76RL01830
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Applied Energy
Additional Journal Information:
Journal Volume: 209; Journal Issue: C; Journal ID: ISSN 0306-2619
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; building energy demand; representative cities; BEND; EnergyPlus; weather

Citation Formats

Burleyson, Casey D., Voisin, Nathalie, Taylor, Z. Todd, Xie, Yulong, and Kraucunas, Ian. Simulated building energy demand biases resulting from the use of representative weather stations. United States: N. p., 2017. Web. doi:10.1016/J.APENERGY.2017.08.244.
Burleyson, Casey D., Voisin, Nathalie, Taylor, Z. Todd, Xie, Yulong, & Kraucunas, Ian. Simulated building energy demand biases resulting from the use of representative weather stations. United States. doi:10.1016/J.APENERGY.2017.08.244.
Burleyson, Casey D., Voisin, Nathalie, Taylor, Z. Todd, Xie, Yulong, and Kraucunas, Ian. 2017. "Simulated building energy demand biases resulting from the use of representative weather stations". United States. doi:10.1016/J.APENERGY.2017.08.244.
@article{osti_1413471,
title = {Simulated building energy demand biases resulting from the use of representative weather stations},
author = {Burleyson, Casey D. and Voisin, Nathalie and Taylor, Z. Todd and Xie, Yulong and Kraucunas, Ian},
abstractNote = {Numerical building models are typically forced with weather data from a limited number of “representative cities” or weather stations representing different climate regions. The use of representative weather stations reduces computational costs, but often fails to capture spatial heterogeneity in weather that may be important for simulations aimed at understanding how building stocks respond to a changing climate. Here, we quantify the potential reduction in temperature and load biases from using an increasing number of weather stations over the western U.S. Our novel approach is based on deriving temperature and load time series using incrementally more weather stations, ranging from 8 to roughly 150, to evaluate the ability to capture weather patterns across different seasons. Using 8 stations across the western U.S., one from each IECC climate zone, results in an average absolute summertime temperature bias of ~4.0 °C with respect to a high-resolution gridded dataset. The mean absolute bias drops to ~1.5 °C using all available weather stations. Temperature biases of this magnitude could translate to absolute summertime mean simulated load biases as high as 13.5%. Increasing the size of the domain over which biases are calculated reduces their magnitude as positive and negative biases may cancel out. Using 8 representative weather stations can lead to a 20–40% bias of peak building loads during both summer and winter, a significant error for capacity expansion planners who may use these types of simulations. Using weather stations close to population centers reduces both mean and peak load biases. Our approach could be used by others designing aggregate building simulations to understand the sensitivity to their choice of weather stations used to drive the models.},
doi = {10.1016/J.APENERGY.2017.08.244},
journal = {Applied Energy},
number = C,
volume = 209,
place = {United States},
year = 2017,
month =
}

Journal Article:
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  • Numerical building models are typically forced with weather data from a limited number of “representative cities” or weather stations representing different climate regions. The use of representative weather stations reduces computational costs, but often fails to capture spatial heterogeneity in weather that may be important for simulations aimed at understanding how building stocks respond to a changing climate. We quantify the potential reduction in bias from using an increasing number of weather stations over the western U.S. The approach is based on deriving temperature and load time series using incrementally more weather stations, ranging from 8 to roughly 150, tomore » capture weather across different seasons. Using 8 stations, one from each climate zone, across the western U.S. results in an average absolute summertime temperature bias of 7.2°F with respect to a spatially-resolved gridded dataset. The mean absolute bias drops to 2.8°F using all available weather stations. Temperature biases of this magnitude could translate to absolute summertime mean simulated load biases as high as 13.8%, a significant error for capacity expansion planners who may use these types of simulations. Increasing the size of the domain over which biases are calculated reduces their magnitude as positive and negative biases may cancel out. Using 8 representative weather stations can lead to a 20-40% overestimation of peak building loads during both summer and winter. Using weather stations close to population centers reduces both mean and peak load biases. This approach could be used by others designing aggregate building simulations to understand the sensitivity to their choice of weather stations used to drive the models.« less
  • Buildings consume more than one third of the world?s total primary energy. Weather plays a unique and significant role as it directly affects the thermal loads and thus energy performance of buildings. The traditional simulated energy performance using Typical Meteorological Year (TMY) weather data represents the building performance for a typical year, but not necessarily the average or typical long-term performance as buildings with different energy systems and designs respond differently to weather changes. Furthermore, the single-year TMY simulations do not provide a range of results that capture yearly variations due to changing weather, which is important for building energymore » management, and for performing risk assessments of energy efficiency investments. This paper employs large-scale building simulation (a total of 3162 runs) to study the weather impact on peak electricity demand and energy use with the 30-year (1980 to 2009) Actual Meteorological Year (AMY) weather data for three types of office buildings at two design efficiency levels, across all 17 ASHRAE climate zones. The simulated results using the AMY data are compared to those from the TMY3 data to determine and analyze the differences. Besides further demonstration, as done by other studies, that actual weather has a significant impact on both the peak electricity demand and energy use of buildings, the main findings from the current study include: 1) annual weather variation has a greater impact on the peak electricity demand than it does on energy use in buildings; 2) the simulated energy use using the TMY3 weather data is not necessarily representative of the average energy use over a long period, and the TMY3 results can be significantly higher or lower than those from the AMY data; 3) the weather impact is greater for buildings in colder climates than warmer climates; 4) the weather impact on the medium-sized office building was the greatest, followed by the large office and then the small office; and 5) simulated energy savings and peak demand reduction by energy conservation measures using the TMY3 weather data can be significantly underestimated or overestimated. It is crucial to run multi-decade simulations with AMY weather data to fully assess the impact of weather on the long-term performance of buildings, and to evaluate the energy savings potential of energy conservation measures for new and existing buildings from a life cycle perspective.« less
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  • We present here a detailed, service-based model of China’s building energy use, nested in the GCAM (Global Change Assessment Model) integrated assessment framework. Using the model, we explore long-term pathways of China’s building energy use and identify opportunities of reducing greenhouse gas emissions. The inclusion of a structural model of building energy demands within an integrated assessment framework represents a major methodological advance. It allows for a structural understanding of the drivers of building energy consumption while simultaneously considering the other human and natural system interactions that influence changes in the global energy system and climate. We also explore amore » range of different scenarios to gain insights into how China’s building sector might evolve and what the implications might be for improved building energy technology and carbon policies. The analysis suggests that China’s building energy growth will not wane anytime soon, although technology improvement will put downward pressure on this growth. Also, regardless of the scenarios represented, the growth will involve the continued, rapid electrification of the buildings sector throughout the century, and this transition will be accelerated by the implementation of carbon policy.« less