Multi-year and reference year weather data for building energy labelling in north Italy climates

Pernigotto, Giovanni; Prada, Alessandro; Cóstola, D Daniel; Gasparella, Andrea; Hensen, Jlm Jan

doi:10.1016/j.enbuild.2013.12.012

Cited by 46 publications

(36 citation statements)

References 25 publications

(25 reference statements)

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“…Second, a typical year does not necessarily represent the average value of the historic long-term climate and cannot reflect the variation and uncertainty inherent in the actual weather data. Some studies have shown that the building energy use predicted by a typical year followed the long-term mean quite well [34], [45], [46], whereas the conclusion from other research was that the representativeness of a typical year's results could vary significantly in the considered locations [38]. Moreover, climate is such a complex and changeable phenomenon in which much variety can be found from year to year.…”

Section: Introductionmentioning

confidence: 83%

“…Crawley [37] indicated that single year, TRY-type weather data cannot represent typical long-term weather; instead, a synthetic year such as TMY2 or WYEC2 was recommended. Among the methods for deriving TMY files, there is no agreement either on the number of weather parameters to use or on the weighting of the weather parameters [38]. Some authors even claim that the generation of typical year weather data is not very sensitive to the weighting of different weather variables [39].…”

Section: Introductionmentioning

confidence: 99%

“…Hui et al [51] presented a pilot study in Hong Kong regarding long-term building energy performance using multiyear weather data. Large-scale simulations were conducted by Hong et al [52] to analyze the sensitivity of building energy use and peak demand in relation to yearly variations in weather with multidecade Actual Meteorological Year (AMY) weather data from 17 climate zones in the U.S. Pernigotto et al [38] investigated the annual variability in energy results by using multiyear simulations for five locations in Italy and analyzed its effects on the building envelope energy ratings.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of typical year and multiyear building simulations using a 55-year actual weather data set from China

et al. 2017

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Abstract:Weather has significant impacts on the thermal environment and energy use in buildings. Thus, accurate weather data are crucial for building performance evaluations. Traditionally, typical year data inputs are used to represent long-term weather data. However, there is no guarantee that a single year represents the changing climate well. In this study, the long-term representation of a typical year was assessed by comparing it to a 55-year actual weather data set. To investigate the weather impact on building energy use, 559 simulation runs of a prototype office building were performed for 10 large cities covering all climate zones in China. The analysis results demonstrated that the weather data varied significantly from year to year. Hence, a typical year cannot reflect the variation range of weather fluctuations. Typical year simulations overestimated or underestimated the energy use and peak load in many cases. With the increase in computational power of personal computers, it is feasible and essential to adopt multiyear simulations for full assessments of long-term building performance, as this will improve decision-making by allowing for the full consideration of variations in building energy use.

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Section: Introductionmentioning

confidence: 83%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparison of typical year and multiyear building simulations using a 55-year actual weather data set from China

et al. 2017

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“…From this comparison, Trento and Monza reference years, developed starting, respectively, from 10-and 9-year series, showed the largest impact on heating needs and, for this reason, have been selected for the current analysis. 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.…”

Section: Development Of Reference Yearsmentioning

confidence: 99%

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

et al. 2017

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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.

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“…This objective methodology allowed the choice of the most disadvantaged buildings for choosing the best retrofit actions. A steady-state analysis has been preferred to a dynamic one as the latter is more demanding and time-consuming and requires a larger amount of data input (including the TRY-Test Reference Year which is often not available; see Pernigotto et al [25]). Such input is often difficult to achieve-for example, a precise occupant schedule (Hoes et al [26]).…”

Section: Analysis Of the Building And Its Equipment And Energy Classimentioning

confidence: 99%

A Decision Making Tool for a Comprehensive Evaluation of Building Retrofitting Actions at the Regional Scale

et al. 2016

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Buildings in Europe account for 40% of total primary energy consumption and 36% of CO 2 emissions. Nearly one-half of the building stock was built before modern energy efficiency standards and need urgent renovation. Urban retrofitting has emerged as a crucial factor for bringing about a radical change, the new construction rate being lower than 1%. Nevertheless, an accepted and consolidated methodology for refurbishing the existing housing stock is still lacking. The study presents an operating methodology for the optimization of the retrofitting process, based on energy efficiency and cost-effectiveness, as well as users' comfort, in the building asset of ITEA SpA, the social housing institute for the Province of Trento (Italy), which manages more than 600 buildings. The research consists of the following stages: (1) definition of building classes, similar in age, dimension, typology, construction system and location; (2) analysis of plant systems and recognition of cases significant for classifying buildings in term of energy class; (3) identification of possible improvements and related cost-benefits; and (4) extension of the results to the whole building class. A tool is here proposed, intended for use by ITEA in order to set medium-and long-term plans. The tool does not consider only the effective sustainability of the controlling body intervention but also the final users' full satisfaction.

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Multi-year and reference year weather data for building energy labelling in north Italy climates

Cited by 46 publications

References 25 publications

Comparison of typical year and multiyear building simulations using a 55-year actual weather data set from China

Comparison of typical year and multiyear building simulations using a 55-year actual weather data set from China

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

A Decision Making Tool for a Comprehensive Evaluation of Building Retrofitting Actions at the Regional Scale

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