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Decarbonization goals in the United States electricity sector are increasing the levels of renewable energy generation in the electricity supply system, and are driving increased attention to building electrification, which will increase the magnitude and shift the timing of the electricity system peak. These changes are motivating new approaches to coordinate building electricity demand with low-carbon renewable generation, elevating the importance of demand flexibility (DF) in buildings and the need to quantify the temporal impacts of DF. In this paper, we first characterize the hourly predictive accuracy of six commonly used baseline models in an application context of quantifying building-level load shift. Our analysis revealed insights such as hours of the day (afternoons), periods of the week (weekends), and seasons (summer) that were predicted with more accuracy than other time periods. In addition, the analysis showed tendencies toward overprediction or underprediction of load. Secondly, we provide the first published investigation of baseline erosion from repeated dispatch of building load shifting. We observed that as the baseline period is pushed back further from the prediction day, the distribution of errors across baseline model predictions increases, with notable inflection points near the three-week erosion point for two of the three models. [ABSTRACT FROM AUTHOR]

With the expansion of advanced metering and increased use of energy analytics tools, the energy efficiency community has begun to explore the application of advanced measurement and verification (or ‘M&V 2.0′) technologies. Current literature recognizes their promise, but does not offer in-depth assessment of technical underpinnings. This paper assesses the state of the technology and its application. Sixteen commercially available technologies were characterized and combined with a national review of their use. [ABSTRACT FROM AUTHOR]

In 2005, the largest field study to-date concluded that automated dimming controls in side-lighted spaces consistently underperformed relative to expectation, and were still to be considered an 'emerging' technology. Motivated by a desire to accelerate the adoption and acceptance of such controls in, this paper details a field-measured assessment of a digital daylighting system recently introduced to the US market. Two intentionally challenging offices were retrofit with controls, and automated data acquisition hardware, and system performance was evaluated across several parameters throughout a twelve-month period. The field data and user interviews indicate that the system was relatively straightforward to commission compared to other dimming controls, and overall was well received by the occupants. Energy savings in the private and open-plan installations were between 17 percent and 20 percent. In the private office, the pre-existence of occupancy sensing, and automatic-ON in the retrofit system precluded even deeper energy savings, whereas false triggering of the occupancy sensor limited the achieved savings in the open plan space. The controller was largely able to maintain target illuminance on the worksurface, although increased daylight availability led to increased variability and a 100-lx increase between noon and 2PM. Key recommendations to increase the acceptance of similar systems include the provision of shielding options for occupancy sensors, manual-ON options for private offices, remote-access commissioning tools, and improved clarity of written materials describing system function, operation, and installation. [ABSTRACT FROM AUTHOR]

• FDD tools are increasingly adopted in commercial buildings. • Using FDD tools to correct hunting faults can mitigate issues cost-effectively. • We developed and implemented automated control hunting correction in FDD tools. • The algorithm was tested in nine VAV boxes and successfully eliminated hunting faults. • We discussed challenges and future research areas for scaling the technology up. Control hunting due to improper proportional–integral–derivative (PID) parameters in the building automation system (BAS) is one of the most common faults identified in commercial buildings. It can cause suboptimal performance and early failure of heating, ventilation, and air conditioning (HVAC) equipment. Commercial fault detection and diagnostics (FDD) software represents one of the fastest growing market segments in smart building technologies in the United States. Implementation of PID retuning procedures as an auto-correction algorithm and integration into FDD software has the potential to mitigate control hunting across a heterogeneous portfolio of buildings with different BAS in a scalable way. This paper presents the development, implementation, and field testing of an automated control hunting fault correction algorithm based on lambda tuning open-loop rules. The algorithm was developed in a commercial FDD software and successfully tested among nine variable air volume boxes in an office building in the United States. The paper shows the feasibility of using FDD tools to automatically correct control hunting faults, discusses scalability considerations, and proposes a path forward for the HVAC industry and academia to further improve this technology. [ABSTRACT FROM AUTHOR]