Data-Informed Building Energy Management (DIBEM) Towards Ultra-Low Energy Buildings

26 Pages Posted: 30 Sep 2022

See all articles by Jung Min Han

Jung Min Han

Harvard University - Harvard Graduate School of Design

Sunghwan Lim

Harvard University - Harvard Graduate School of Design

Ali Malkawi

Harvard University - Harvard Graduate School of Design

Xu Han

Harvard University - Harvard Graduate School of Design

Elence Xinzhu Chen

Harvard University - Harvard Graduate School of Design

Shide Salimi

Harvard University - Harvard Graduate School of Design

Tor Helge Dokka

Skanska Technology

Tine Hegli

Snohetta

Kristian Edwards

Snohetta

Abstract

To support building operations in reaching ultra-low energy targets, this paper proposes a data-informed building energy management (DiBEM) framework to improve energy efficiency systematically and continuously at the operation stage. Specifically, it has two key features including data-informed energy-saving potential identification and data-driven model-based energy savings evaluation. The paper demonstrates the proposed DiBEM with a detailed case study of an office and living laboratory building located in Cambridge, Massachusetts called HouseZero. It focuses on revealing the performance of the energy-efficient interventions from two-years’ building performance monitoring data, as well as evaluating energy savings from the interventions based on the data-driven approach. With Year1 as baseline, several interventions are proposed for Year2 including improvements to controls and operation settings, encouragement of occupants’ behavior for energy savings and hardware retrofitting. These were deployed to heating/cooling, domestic hot water, lighting, plug and other loads and photovoltaic (PV) systems. To quantify the impacts of different interventions on energy end uses, several data-driven models are developed. These models utilize linear regression, condition model and machine learning techniques. Consequently, the heating/cooling energy consumption that was already ultra-low in Year1 (12.8 kWh/m 2 ) is further reduced to 9.7 kWh/m 2 in Year2, while the indoor thermal environment is well maintained. The domestic hot water energy is reduced from 2.3 kWh/m 2 to 1.2 kWh/m 2 . The lighting energy is only increased from 0.3 kWh/m 2 in pandemic operations without occupancy in Year1 to 0.8 kWh/m 2 in partial normal operations in Year2, while the indoor illuminance level meets occupants’ requirements. Combined with other relatively constant loads and reducing plug and other loads due to COVID building operation restrictions, the total energy use intensity is thereby reduced from 54.1 kWh/m 2 to 42.8 kWh/m 2 , where 5.4 kWh/m 2 of energy reduction for Year2 is estimated to be contributed by the energy efficient interventions. PV generation is 36.1 kWh/m 2 , with an increase of 1.4 kWh/m 2 from a new inverter. In summary, this paper demonstrates the use of DiBEM through a detailed case study and long-term monitoring data as evidence to achieve ultra-low energy operations.

Keywords: Ultra-low energy buildings, Data-informed building energy management, Energy-efficient interventions, Energy savings evaluation

Suggested Citation

Han, Jung Min and Lim, Sunghwan and Malkawi, Ali and Han, Xu and Chen, Elence Xinzhu and Salimi, Shide and Dokka, Tor Helge and Hegli, Tine and Edwards, Kristian, Data-Informed Building Energy Management (DIBEM) Towards Ultra-Low Energy Buildings. Available at SSRN: https://ssrn.com/abstract=4232295 or http://dx.doi.org/10.2139/ssrn.4232295

Jung Min Han

Harvard University - Harvard Graduate School of Design ( email )

Sunghwan Lim

Harvard University - Harvard Graduate School of Design ( email )

Ali Malkawi

Harvard University - Harvard Graduate School of Design ( email )

Xu Han (Contact Author)

Harvard University - Harvard Graduate School of Design ( email )

Elence Xinzhu Chen

Harvard University - Harvard Graduate School of Design ( email )

Shide Salimi

Harvard University - Harvard Graduate School of Design ( email )

Tor Helge Dokka

Skanska Technology ( email )

Tine Hegli

Snohetta ( email )

Kristian Edwards

Snohetta ( email )

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