Robustness, Multi-Objective Optimization, Weather Data, Building Energy Simulation
The interest in energy refurbishment has grown up in the last few years, since noticeable energy savings can be achieved through energy saving measures (ESM) applied to the existing building stock. In this respect, one of the best opportunities to promote the energy renovation of the existing building is to define cost-effective solutions. The recast of the European energy performance of building directive (EPBD-recast) and the Commission Delegated Regulation EU 244/2012 underline the necessity to define “energy performance level which leads to the lowest cost during the estimated economic life-cycle” of a building. The choice of the optimal solution addresses the problem of optimizing two or more conflicting goals such as the net present value (NPV) and the achievable energy performance. For this reason multi-objective optimizations are often applied to building energy simulation. However, the higher capabilities in calculating detailed outputs imply more complex and detailed inputs. In this regard, the representativeness of weather inputs is crucial to ensure the reliability of energy simulation results. In fact, the length of the multi-year weather data series and the methodology used for the typical month selection largely influence the results of the reference year development process. Although this topic has been widely discussed in the literature, little is yet known about the robustness of the optimal solutions obtained from multi-objective optimization analysis to the variation of the quality of the weather data used. This information could also be relevant to understand if the optimal solution obtained with historical weather data could be undermined by future climate changes. Aiming to provide objective confidence levels of the multi-objective optimization analyses, in this work we investigate the extent to which the weather data used for building energy simulations can affect the optimal solutions. With this purpose, several MOO are carried out both with multi-years energy simulations and with the test reference years. In particular, the adopted test reference year was developed according to EN ISO 15927-4:2005 starting from the hourly weather data collected in the meteorological station of Trento, in northern Italy. Different lengths of data series were adopted in order to model the different levels of data availability. Furthermore, semi-detached houses penthouses and intermediate flat in multi-story buildings are analyzed with the purpose of broaden the representativeness of the conclusions.