Demonstrating the reliability of randomized measurement and verification for switchable control retrofits using a large open-source dataset

Conventional measurement and verification (M&V) methods for estimating energy savings rely on comparing pre- and post-retrofit performance. They are often time-consuming and unreliable, especially when non-routine events, such as step changes or more gradual changes in building operation, occur during the M&V process. When those events are unrelated to the retrofit intervention and significantly affect building energy consumption, the results will be confounded when the analyst applies the conventional M&V method. In this study, we demonstrated that switchable interventions, such as most HVAC control retrofits, can benefit from a new M&V method that randomly samples whether to implement the baseline or the intervention strategy at a fixed interval (e.g., daily). We tested this novel randomized M&V method on a large public dataset (hourly energy data over 2 years for 639 buildings) covering various climate zones and commercial building types, using a virtual chilled water supply temperature reset based on outdoor weather as the intervention. The results show that, compared to the conventional method, the randomized method provides more accurate savings estimations with a median of 74% accuracy improvement and is faster (typically 36 weeks instead of 104 weeks, ∼65% reduction in duration). Additionally, we found that when non-routine events are present (e.g., occupancy pattern change), the randomized method estimates savings that are much closer to the ground-truth values than the conventional method, demonstrating significantly improved reliability. We also assessed the impact of normalizing for different weather, starting the M&V at different dates of the year, continuing randomization with a different sampling ratio after satisfying all stopping criteria, and dropping samples affected by carryover effects when switching between strategies. For each scenario, we identified the optimal sampling interval using the large dataset.