TY - GEN
T1 - Comparing deterministic and probabilistic non-operational building energy modelling
AU - Cousins-Jenvey, Bengt
AU - Walker, Pete
AU - Shea, Andrew
AU - Sykes, Judith
AU - Johansson, Anders
PY - 2014/1/1
Y1 - 2014/1/1
N2 - There is a lack of consensus about whether researchers and practitioners should use a deterministic single value or probabilistic distribution of values for each input when modelling the life cycle of a building. This study produces a direct comparison of the two approaches by modelling the nonoperational life cycle energy of three buildings deterministically and probabilistically to explore whether the two approaches produce different conclusions. A detailed method describes the model - its structure, formulae and the best-case, typical and worst-case inputs - with supporting references. This detail provides a thorough explanation of why probabilistic modelling suggests that non-operational energy could be 28-44% lower or 48-283% higher than the original values and why a significant shift in the distribution of non-operational life cycle energy is possible. When used deterministically, the model suggests non-operational energy use is greatest during the product phase. However, when used probabilistically, the model highlights the risk that short component lives and long distance transport by road can significantly increase non-operational energy during the use, construction and end of life phases. The study discusses how future modelling should address a number of uncertainties so that it is more useful for researchers and practitioners.
AB - There is a lack of consensus about whether researchers and practitioners should use a deterministic single value or probabilistic distribution of values for each input when modelling the life cycle of a building. This study produces a direct comparison of the two approaches by modelling the nonoperational life cycle energy of three buildings deterministically and probabilistically to explore whether the two approaches produce different conclusions. A detailed method describes the model - its structure, formulae and the best-case, typical and worst-case inputs - with supporting references. This detail provides a thorough explanation of why probabilistic modelling suggests that non-operational energy could be 28-44% lower or 48-283% higher than the original values and why a significant shift in the distribution of non-operational life cycle energy is possible. When used deterministically, the model suggests non-operational energy use is greatest during the product phase. However, when used probabilistically, the model highlights the risk that short component lives and long distance transport by road can significantly increase non-operational energy during the use, construction and end of life phases. The study discusses how future modelling should address a number of uncertainties so that it is more useful for researchers and practitioners.
UR - http://www.scopus.com/inward/record.url?scp=85086462690&partnerID=8YFLogxK
M3 - Chapter in a published conference proceeding
AN - SCOPUS:85086462690
T3 - 30th International PLEA Conference: Sustainable Habitat for Developing Societies: Choosing the Way Forward - Proceedings
SP - 115
EP - 122
BT - 30th International PLEA Conference
A2 - Rawal, Rajan
A2 - Manu, Sanyogita
A2 - Khadpekar, Nirmala
PB - CEPT University Press
T2 - 30th International on Passive and Low Energy Architecture Conference - Sustainable Habitat for Developing Societies: Choosing the Way Forward, PLEA 2014
Y2 - 16 December 2014 through 18 December 2014
ER -