Academic Research
Our team are experts at building science, machine learning, and artificial intelligence. Our Research & Development team shares our research in academic journals.
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Using PeopleHour For Occupant-Centric Office Building Performance Assessment
Measuring and benchmarking office building performance is crucial for enhancing energy efficiency, reducing environmental impact, and improving occupant productivity. Traditional Energy Use Intensity (EUI) metrics and benchmarking methods developed based on them have limitations in accounting for factors like occupancy, can hardly be explainable, and lack evolution with the advent of more real-time data. This paper introduces a set of metrics for building performance based on PeopleHour, which incorporates both the number and duration of occupancy to provide a more occupant-centric perspective on office building performance. By adjusting EUI and other related metrics to reflect building performance normalized by occupancy, we offer a more accurate measure of office building efficiency. Using sample office building data from Nantum OS, we demonstrate how PeopleHour-adjusted metrics reveal insights that traditional methods may overlook, particularly during significant occupancy changes before and after the COVID-19 pandemic. This approach emphasizes the importance of occupancy-driven operations especially as the shift of work mode and office building uses after the pandemic. It suggests that PeopleHour can enhance energy benchmarking practices, leading to more informed decisions for improving building performance across various sectors.
Gulai Shen, John J. Gilbert IV, Ali Mehmani
Nantum AI, New York, NY, United States of America
Harvard University, Cambridge, MA, United States of America
Highlights
Introduces PeopleHour concept for occupant-centric building performance assessment.
Uses high-frequency occupancy data in office building performance analysis.
Shows PeopleHour metrics reveal insights overlooked by traditional metrics.
Compares pre- and post-pandemic building performance with new metrics.
Abstract
Measuring and benchmarking office building performance is crucial for enhancing energy efficiency, reducing environmental impact, and improving occupant productivity. Traditional Energy Use Intensity (EUI) metrics and benchmarking methods developed based on them have limitations in accounting for factors like occupancy, can hardly be explainable, and lack evolution with the advent of more real-time data. This paper introduces a set of metrics for building performance based on PeopleHour, which incorporates both the number and duration of occupancy to provide a more occupant-centric perspective on office building performance. By adjusting EUI and other related metrics to reflect building performance normalized by occupancy, we offer a more accurate measure of office building efficiency. Using sample office building data from Nantum OS, we demonstrate how PeopleHour-adjusted metrics reveal insights that traditional methods may overlook, particularly during significant occupancy changes before and after the COVID-19 pandemic. This approach emphasizes the importance of occupancy-driven operations especially as the shift of work mode and office building uses after the pandemic. It suggests that PeopleHour can enhance energy benchmarking practices, leading to more informed decisions for improving building performance across various sectors.
Indoor environmental wellness index (IEW-Index): Towards intelligent building systems automation and optimization
The impact of indoor environment quality (IEQ) on occupants’ wellness and productivity is substantial. Simultaneously, due to buildings’ significant role as one of the largest energy consumers, there is a pressing need for improved energy efficiency and intelligence in building design and operation. This paper introduces a novel indoor environmental wellness index (IEW-Index) as a real-time key performance indicator (KPI) derived from controllable factors associated with wellness. The main objective of this index is to develop a universally applicable metric for IEQ, incorporate real-time data and personalized settings, and facilitate the real-time optimization of building control systems. In addition, a novel framework based on a multi-agent system (MAS) for building control systems is introduced, encompassing a detailed design of components and communication protocols. The IEW-Index’s effectiveness has been successfully demonstrated through three real-world case studies formulated using the developed MAS framework. The findings indicate a direction for creating a sustainable building automation system that is intelligent and autonomous.