Abstract

The simulation of the energy consumptions in an hourly regime is necessary in order to perform calculations on residential buildings of particular relevance for volume or for architectural features. In such cases, the simplified methodology provided by the regulations may be inadequate, and the use of software like EnergyPlus is needed. To obtain reliable results, usually, significant time is spent on the meticulous insertion of the geometrical inputs of the building, together with the properties of the envelope materials and systems. Less attention is paid to the climate database. The databases available on the EnergyPlus website refer to airports located in rural areas near major cities. If the building to be simulated is located in a metropolitan area, it may be affected by the local heat island, and the database used as input to the software should take this phenomenon into account. To this end, it is useful to use a meteorological model such as the Weather Research and Forecasting (WRF) model to construct an appropriate input climate file. A case study based on a building located in the city center of Rome (Italy) shows that, if the climatic forcing linked to the heat island is not considered, the estimated consumption due to the cooling is underestimated by 35–50%. In particular, the analysis and the seasonal comparison between the energy needs of the building simulated by EnergyPlus, with the climatic inputs related to two airports in the rural area of Rome and with the inputs provided by the WRF model related to the center of Rome, show discrepancies of about (i) WRF vs. Fiumicino (FCO): Δ = −3.48% for heating, Δ = 49.25% for cooling; (ii) WRF vs. Ciampino (CIA): Δ = −7.38% for heating, Δ = +35.52% for cooling.

Highlights

  • The residential building sector accounts for a significant share of energy consumption in each industrialized country, with percentages that exceed one-third of total energy needs

  • Depending on the purpose of the energy analysis, and on the importance and complexity of the building, the legislation allows the use of simplified calculation methods or not. In cases where such simplified methods cannot be applied in the quantification of the annual primary energy needs for air conditioning and production of domestic hot water, the legislation requires the use of a software able to simulate the behavior of the “building/plant” system in a dynamic regime [18]

  • Thanks to the coupling between Weather Research and Forecasting (WRF) and EnergyPlus, the energy performance computed for the same building using climate databases provided for Rome by EnergyPlus and data obtained throughout WRF runs were compared

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Summary

Introduction

The residential building sector accounts for a significant share of energy consumption in each industrialized country, with percentages that exceed one-third of total energy needs. Depending on the purpose of the energy analysis, and on the importance and complexity of the building, the legislation allows the use of simplified calculation methods or not In cases where such simplified methods cannot be applied in the quantification of the annual primary energy needs for air conditioning and production of domestic hot water, the legislation requires the use of a software able to simulate the behavior of the “building/plant” system in a dynamic regime [18]. According to Chan [35], the construction of suitable weather files from urban experimental campaigns could be the only method used to calculate energy consumption for buildings subject to the effects of the urban heat island In this context, approximations introduced using non-realistic input climate databases is unacceptable for those who need to perform precise calculations and who, for this reason, use a dynamic computation. Ciampino (CIA)); and (ii) input climate data sampled in rural areas compared to the city center (characterized by the heat island phenomenon)

Modeling Tools
The Case Study
Aerial photo of themetropolitan
Analysis of Climatic Inputs and Evaluation of the WRF Model
Weather
Analysis of Energy Needs
Conclusions
Findings
Methods
Full Text
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