The impact of weather data sources on building energy retrofit design: case study in heating-dominated climate of Italian backcountry. Issue 3 (3rd May 2020)

• Record Type: Journal Article
• Authors: De Masi, Rosa Francesca; Gigante, Antonio; Ruggiero, Silvia; Vanoli, Giuseppe Peter
• Is Part Of: Journal of building performance simulation. Volume 13:Issue 3(2020)
• Page Start: 264
• Page End: 284
• Publication Date: 2020-05-03
• Subjects: Weather file -- climate change -- building energy retrofit -- energy performance -- simple payback time; 690.0113
• DOI: 10.1080/19401493.2020.1725131
• Languages: English
• ISSNs: 1940-1493

Abstract

ABSTRACT: This study proposes a replicable approach for evaluating the impact of different weather data sources during the building refurbishment. Starting from a deep energy diagnosis, the numerical model is calibrated by using the weather data and the energy billings of the same year. Then, the performance gap is evaluated due to the adoption of conventional or new weather files defined with data recently monitored in areas with different levels of urbanization. The final step is a sensitivity analysis on the energy, environmental and economic indexes for the refurbishment interventions. The proposed approach is applied to the student dormitory of the University of Molise, located in Campobasso, a city of south Italy with oceanic climate according to Köppen classification. The results highlight significant deviations on the estimated energy consumption as well as on the energetic and environmental indexes of the refurbishment design. Instead, the impact on the economic index is quite limited. Highlights The weather data of rural and urban stations are compared with typical weather files. The UHI effect in heating-dominated climate in central Italy is proved. The mathematical relation between HDD/CCD and weather data evolution is found. The impact of different weather files on building energy performance prevision is studied. Inaccuracies in energy saving, environmental and economic indexes are quantified.