Development of test reference year using ISO 15927-4 and the influence of climatic parameters on building energy performance

Kim, Sumin; Zirkelbach, Daniel; Künzel, Hartwig M.; Lee, Jeong-Hun; Choi, Jeongman

doi:10.1016/j.buildenv.2016.12.037

Cited by 24 publications

(7 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Monza, the differences are less marked and, for solutions with natural ventilation, they are even negligible. The minimum EP h and NPV are found with RY 5 , coherently with the analysis of the existing buildings' performance, and the maximum ones for RY 3 and RY 6 .…”

Section: Shapes Of the Pareto Frontssupporting

confidence: 51%

“…In particular, for the sample of buildings considered in [17,27], in Trento the annual energy needs for space heating varied from a minimum of −9.26% to a maximum of +4.28% with respect to the multi-year averages and in Monza from −8.23% to +11.53%. For the climates studied in [27], simulation results obtained with RY 5 and RY 6 had generally a better accuracy with respect to those with multi-year series. However, none outperformed the others for all climates: for those considered in this study, the largest representativeness was registered for RY 5 and RY 6 in Trento (respectively, with +0.52% and +0.79%) and for RY 2 and RY 6 in Monza (respectively, with −0.02% and +0.7%).…”

Section: Development Of Reference Yearsmentioning

confidence: 93%

“…For the climates studied in [27], simulation results obtained with RY 5 and RY 6 had generally a better accuracy with respect to those with multi-year series. However, none outperformed the others for all climates: for those considered in this study, the largest representativeness was registered for RY 5 and RY 6 in Trento (respectively, with +0.52% and +0.79%) and for RY 2 and RY 6 in Monza (respectively, with −0.02% and +0.7%).…”

Section: Development Of Reference Yearsmentioning

confidence: 93%

“…Tables 5 and 6 report, as examples, the EEMs optima for the different building configurations in Trento. Considering the cost optima, the impact of RY on the insulation thicknesses is limited to 2 cm in many configurations, except for example for the south-oriented intermediate flat, for which 14 cm are recommended for the vertical walls' insulation with RY 4 and 18 cm with RY 5 and RY 6 . All RYs lead to the same results for south-oriented buildings in terms of window, boiler and ventilation preferences.…”

Section: Economic and Energy Optimal Solutionsmentioning

confidence: 99%

“…In a previous work [27], four widespread approaches based on Finkelstein-Schafer statistics were selected and two new methods proposed. Six types of reference years were analyzed: (RY 1 ) EN ISO 15927-4 [20], (RY 2 ) Wilcox and Marion's method [25], (RY 3 ) the method by Pissimanis et al [22], (RY 4 ) the minimum Finkelstein-Schafer statistic, (RY 5 ) Best rank I, and (RY 6 ) Best rank II. Their representativeness with respect to the multi-year weather data series was discussed both in terms of weather variables and in terms of impact on the building energy needs for space heating and cooling.…”

Section: Development Of Reference Yearsmentioning

confidence: 99%

See 4 more Smart Citations

Impact of Reference Years on the Outcome of Multi-Objective Optimization for Building Energy Refurbishment

et al. 2017

View full text Add to dashboard Cite

Abstract:There are several methods in the literature for the definition of weather data for building energy simulation and the most popular ones, such as typical meteorological years and European test reference years, are based on Finkelstein-Schafer statistics. However, even starting from the same multi-year weather data series, the developed reference years can present different levels of representativeness, which can affect the simulation outcome. In this work, we investigated to which extent the uncertainty in the determination of typical weather conditions can affect the results of building energy refurbishment when cost-optimal approach is implemented for the selection of energy efficiency measures by means of the NSGA-II genetic algorithm coupled with TRNSYS simulations. Six different reference years were determined for two north Italy climates, Trento and Monza, respectively in the Alpine and in the continental temperate regions. Four types of energy efficiency measures, related to both building envelope and HVAC system, were applied to six existing building typologies. Results showed how the choice of reference year can alter the shape of the Pareto fronts, the number of solutions included and the selection among the alternatives of the energy efficiency measures, for the entire front and, in particular, for energy and economic optima.

show abstract

Section: Shapes Of the Pareto Frontssupporting

confidence: 51%