Mirzabeigi Shayan, Soltanian-Zadeh Sameeraa, Krietemeyer Bess, Dong Bing, Zhang Jianshun Jensen
Department of Mechanical and Aerospace Engineering, Syracuse University, Syracuse, NY, USA.
Department of Sustainable Resources Management, State University of New York College of Environmental Science and Forestry, Syracuse, NY, USA.
Sci Data. 2025 Jun 18;12(1):1022. doi: 10.1038/s41597-025-05355-4.
This paper presents a unique monitored dataset from the pre-retrofit condition of single-family attached residences that were selected for demonstrating an integrated energy efficiency retrofitting approach in Syracuse, New York. The dataset includes whole-building energy consumption, indoor and outdoor environmental parameters, building envelope performance characteristics as well as occupant behaviors. It spans twelve months of high-frequency monitoring and comprises over 490 data files collected from fourteen apartments in two occupied residential buildings. The metadata model of the building systems and sensors were created using the Brick schema. A data curation was applied to clean and organize the raw data as research-grade dataset. This dataset can be used in various applications - building energy benchmarking and model calibration, indoor air quality and thermal comfort analysis, energy usage analysis, building envelope performance prediction, occupant behavior analytics, and occupant-centric retrofit strategies development - to boost the performance of existing buildings for reducing energy consumption, energy costs, greenhouse gas emissions, and improving indoor environmental quality and occupants' satisfaction.
本文展示了一个独特的监测数据集,该数据集来自纽约州锡拉丘兹市为展示集成式能源效率改造方法而挑选的独栋联排住宅的改造前状况。该数据集包括整栋建筑的能源消耗、室内和室外环境参数、建筑围护结构性能特征以及居住者行为。它涵盖了十二个月的高频监测数据,包含从两栋有人居住的住宅楼中的十四套公寓收集的490多个数据文件。建筑系统和传感器的元数据模型是使用Brick模式创建的。通过数据整理将原始数据清理并整理为研究级数据集。该数据集可用于各种应用——建筑能源基准测试和模型校准、室内空气质量和热舒适度分析、能源使用分析、建筑围护结构性能预测、居住者行为分析以及以居住者为中心的改造策略制定——以提高现有建筑的性能,从而降低能源消耗、能源成本、温室气体排放,并改善室内环境质量和居住者满意度。