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.

Building energy need simulations are usually performed using input files that contain information about the averaged weather data based on historical patterns. Therefore, the simulations performed are not able to provide information about possible future scenarios due to climate change. In this work, future trends of building energy demands due to the climate change across Europe were studied by comparing three time steps (present, 2050, and -2080) in three different European cities, characterized by different Köppen-Geiger climatic classes. A residential building with modern architectural features was taken into consideration for the simulations. Future climate conditions were reached by applying the effects of climate changes to current hourly meteorological data though the climate change tool world weather file generator (CCWorldWeatherGen) tool, according to the guidelines established by the Intergovernmental Panel on Climate Change. In order to examine the resilience of the building, the simulations carried out were compared with respect to: peak power, median values of the power, and energy consumed by heating and cooling system. The observed trend shows a general reduction in the energy needs for heating (–46% for Aberdeen, –80% for Palermo, –36% for Prague in 2080 compared to the present) and increase (occurrence for Aberdeen) in cooling requirements. These results imply a revaluation of system size.

In this study, the conditions of visual comfort were assessed in an indoor environment intended for students. For this reason, a transversal field survey was carried out in two libraries, one at the Sapienza University of Rome (Italy) and another at the University of Granada (Spain). During the analysis, measurement of lighting engineering variables such as horizontal illuminance, corneal illuminance, and luminance was carried out at the same time. The randomly selected individuals were asked to compile the structured questionnaires voluntarily at both sites. The first part of the questionnaire covered personal information, such as gender, age, eye color, etc. While the latter part evaluated the perception and visual preference of interviewees. In particular, we analyzed the visual perception on the basis of a five-point scale (-2÷+2), where positive and negative values identified visual comfort and discomfort, respectively. Each point was related to both related measured values of the lighting project variables and values of the personal data derived from the questionnaires. The subsequent statistical analysis (carried out through parameters and methodologies such as evaluation of the Variance Inflation Factor, Multicollinearity and Best Subsets Analysis) allowed the development of a new empirical index that is easy to apply, i.e., the Predictive Visual Comfort Index (PVCI). Some of the independent variables included horizontal illuminance, corneal illuminance, and age. In the design phase, once the other variables had been set, it was useful to determine the necessity of corneal illuminance to ensure visual comfort. The influence of each variable on the index and visual comfort was also quantified. Header of solarlits.com