Making energy simulation easier for future climate - Synthesizing typical and extreme weather data sets out of regional climate models (RCMs)
Nik, Vahid M. LU
(2016)
In Applied Energy
177.
p.204-226
- Abstract
Higher availability of future climate data sets, generated by regional climate models (RCMs) with fine temporal and spatial resolutions, improves and facilitates the impact assessment of climate change. Due to significant uncertainties in climate modeling, several climate scenarios should be considered in the impact assessment. This increases the number of simulations and size of data sets, complicating the assessment and decision making. This article suggests an easy-to-use method to decrease the number of simulations for the impact assessment of climate change in energy and building studies. The method is based on synthesizing three sets of weather data out of one or more RCMs: one typical and two extremes. The method aims at... (More)
Higher availability of future climate data sets, generated by regional climate models (RCMs) with fine temporal and spatial resolutions, improves and facilitates the impact assessment of climate change. Due to significant uncertainties in climate modeling, several climate scenarios should be considered in the impact assessment. This increases the number of simulations and size of data sets, complicating the assessment and decision making. This article suggests an easy-to-use method to decrease the number of simulations for the impact assessment of climate change in energy and building studies. The method is based on synthesizing three sets of weather data out of one or more RCMs: one typical and two extremes. The method aims at decreasing the number of weather data sets without losing the quality and details of the original future climate scenarios. The application of the method is assessed for an office building in Geneva and the residential building stock in Stockholm.Results show that using the synthesized data sets provides an accurate estimation of future conditions. Variations and uncertainties of future climate are represented by the synthesized data. In the case of synthesizing weather data using multiple climate scenarios, the number of simulations and the size of data sets are decreased enormously. Combining the typical and extreme data sets enables to have better probability distributions of future conditions, very similar to the original RCM data.
(Less)
- author
- Nik, Vahid M.
LU
- organization
- publishing date
- 2016-09-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Big data, Building, Climate change, Energy simulation, Regional climate models, Weather data
- in
- Applied Energy
- volume
- 177
- pages
- 23 pages
- publisher
- Elsevier
- external identifiers
-
- scopus:84969706240
- wos:000380623900019
- ISSN
- 0306-2619
- DOI
- 10.1016/j.apenergy.2016.05.107
- language
- English
- LU publication?
- yes
- id
- 69df4198-f457-4901-940c-af2eed71348e
- date added to LUP
- 2016-06-23 12:42:42
- date last changed
- 2024-04-19 06:00:26
@article{69df4198-f457-4901-940c-af2eed71348e,
abstract = {{<p>Higher availability of future climate data sets, generated by regional climate models (RCMs) with fine temporal and spatial resolutions, improves and facilitates the impact assessment of climate change. Due to significant uncertainties in climate modeling, several climate scenarios should be considered in the impact assessment. This increases the number of simulations and size of data sets, complicating the assessment and decision making. This article suggests an easy-to-use method to decrease the number of simulations for the impact assessment of climate change in energy and building studies. The method is based on synthesizing three sets of weather data out of one or more RCMs: one typical and two extremes. The method aims at decreasing the number of weather data sets without losing the quality and details of the original future climate scenarios. The application of the method is assessed for an office building in Geneva and the residential building stock in Stockholm.Results show that using the synthesized data sets provides an accurate estimation of future conditions. Variations and uncertainties of future climate are represented by the synthesized data. In the case of synthesizing weather data using multiple climate scenarios, the number of simulations and the size of data sets are decreased enormously. Combining the typical and extreme data sets enables to have better probability distributions of future conditions, very similar to the original RCM data.</p>}},
author = {{Nik, Vahid M.}},
issn = {{0306-2619}},
keywords = {{Big data; Building; Climate change; Energy simulation; Regional climate models; Weather data}},
language = {{eng}},
month = {{09}},
pages = {{204--226}},
publisher = {{Elsevier}},
series = {{Applied Energy}},
title = {{Making energy simulation easier for future climate - Synthesizing typical and extreme weather data sets out of regional climate models (RCMs)}},
url = {{http://dx.doi.org/10.1016/j.apenergy.2016.05.107}},
doi = {{10.1016/j.apenergy.2016.05.107}},
volume = {{177}},
year = {{2016}},
}