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<p>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.</p>

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Energy consumption and IEQ monitoring in two university apartment buildings: Pre-retrofit dataset

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月18日

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  • Shayan Mirzabeigi,
  • Sameeraa Soltanian-Zadeh,
  • Bess Krietemeyer,
  • Bing Dong,
  • Jianshun “Jensen” Zhang

摘要

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.