Abstract
The purpose of this paper is to propagate the input uncertainties of the degree-day method to estimate the building heating energy consumption as numerical intervals. While it is common to use average or expected values (e.g., Typical Meteorological Year) to address the input uncertainties, this practice can only yield the best estimates as single-point values without informing the possible range of variations. After classifying two types of uncertainty as weather variability and imprecision in the degree-day method, this paper proposes the adoption of fuzzy numbers and their arithmetic as the theoretical approach to handle uncertainty. As the degree-day method mainly involves elementary arithmetic (e.g., addition and multiplication), fuzzy number arithmetic can be directly applied to formally process numerical intervals. The proposed method is demonstrated and verified via a building example in Canada, and the interval results are comparable to the variation of heating energy consumption based on the data of outdoor ambient temperatures in 52 years.
Highlights
Degree-days remain one important concept to correlate climate data in the analysis of building energy consumption [1]
As our building application is in the cold weather area, we focus on the heating energy consumption in this study
The purpose of this study is to extend the degree-day method that can propagate the uncertainty of parametric values to estimate building energy consumption as numerical intervals
Summary
Degree-days remain one important concept to correlate climate data in the analysis of building energy consumption [1]. Some of these parameters are subject to uncertainty In this context, the purpose of this study is to extend the degree-day method that can propagate the uncertainty of parametric values to estimate building energy consumption as numerical intervals. One example is the equipment efficiency, which can vary according to operating conditions, and experts can generally approximate its typical numerical range based on the equipment manual and their experience In this context, the purpose of this study is to propagate these two types of uncertainty using fuzzy numbers and their arithmetic [25].
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