• Energy Research

Consistent climate data and reliable sizing methods are fundamental for designing Technical Building Systems. This paper addresses both these aspects with reference to the Italian context analyzing 108 different locations. Regarding the climate data, a quality analysis through the application of rules and filters was performed on the locations. The results showed good data quality, with most sites having high percentages of usable material, although highlighting recursive criticalities in the detection of humidity and wind speed parameters. Regarding the sizing methods, a sensitivity analysis was carried on for the European standard EN ISO 15927-2 to evaluate its performances in selecting the Cooling Design Days in the 108 locations. The analysis highlighted great differences in terms of chosen Design Days when using different climatic parameters, showing at the same time a negligible influence of the wind speed on the selection process. Furthermore, a sizing process for a test building applied to the 108 locations was carried on by using the Cooling Design Days. The results highlighted that the Design Days obtained through the EN ISO method often give counterintuitive sizing outputs, even obtaining higher required powers for lower risk levels, the opposite of how it should be. This implies that a designer should be very careful in evaluating which parameters to use in the Design Day selection and if the output sizing powers obtained through them are reliable for its particular sizing process.

Dynamic thermal simulation is attracting the interest of designers thanks to the availability of numerical codes. However, one of the main problems facing the potential users is hourly-weather data availability. In Italy, the main source for the data is drawn upon the IGDG database. It is provided by the EnergyPlus weather site and the related measurements were collected between 1951 and 1970. A new set of files, gathered between 1989 and 2014, has recently become available. Nevertheless, it needs additional work to be directly implemented in energy simulation codes, which can lead users to download and apply the already available IGDG database files. In order to evaluate the ensuing effects, 52 Italian weather file locations were selected, and two simulations were carried out on seven multifamily-house models considering the difference between the results obtained using the old and new weather data. Since the locations of the two databases do not coincide, a matching method was implemented. This paper compares the simulation results arising from the choice of the weather file pertaining to the two databases. The weather files were selected within a 50 km range and 50 m elevation difference, to minimize misrepresentative results due to different climatic conditions. The models are typical of the most widespread building typology of the Italian building stock. For each model, two building fabric types were considered, the former was poorly insulated, the latter was well insulated thanks to an advanced refurbishment activity. Simulations were carried out with the EnergyPlus software.

This paper investigates the possibility to enhance the thermal characteristics of a building fabric taking into account the economic feasibility in order to avoid useless expensive interventions. The problem consists in realizing an internal insulation system for a single apartment located in Trieste. A genetic optimization has been implemented in order to search a large number of solutions with multiple objects and constraints. EnergyPlus has been used for dynamic building and plant simulation and modeFRONTIER for optimization loop setup. The objectives are the used heating energy consumption and the life cycle refurbishment cost, using a net present value approach. An additional objective has been added in order to guarantee the maximization of the interior useful floor area, which is directly influenced by the internal insulation thickness. Different approaches have been compared, considering actions dealing with opaque surfaces only or considering the replacement of existing windows. The paper demonstrates that an automatic optimization approach can help designers to identify the best suited intervention for building refurbishment.

In G. Pernigotto, F. Patuzzi, A. Prada, V. Corrado, & A. Gasparella (Eds.), Building Simulation Applications BSA 2017 (pp. 233–240).

This paper investigates the influence of a stochastic variation of both energy and economic parameters in an optimization loop applied to a refurbished social housing building. Usually, energy and economic optimization procedures rely on the results of an underlying numerical deterministic model which influences both energy gains and economic figures. However, an analyst must always face the random variation of input and parameter data. The unknown data can represent poor initial information or data that can change in a long time; this is the case of fuel cost and economic indexes in particular. This paper deals with both problems for building refurbishment optimization, the former related to the initial state of a building, and the latter to the energy cost variability. Reliability analysis considers a stochastic variation of parameters looking for solutions that incorporate a risk level; in this case, it deals with optimization objectives related to different impacts on economic, environmental and health aspects. The considered building represents a social house, and the energy reduction measures involve the application of internal insulation layers to the walls and the replacement of existing windows with more efficient ones.

This paper investigates the effect of climatic data on the results in an optimization loop applied to a refurbished social housing building. The considered energy reduction measures involve the application of internal insulation layers to the original walls and the replacement of existing windows with more efficient ones. The optimization relies on the numerical results obtained by the underlying simulation engine, but the results can be heavily influenced by the adopted climatic data. In this study, the impact of the choice of three different TRYs (from the same area of Trieste), applied to multi-objective optimization is investigated. EnergyPlus performs the energy simulation while modeFRONTIER, with python scripting, drives the optimization. The results highlight the differences in the optimal choices a designer can obtain by changing the climatic database