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

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