How Baseline Model Implementation Choices Affect Demand Response Assessments

Abstract

The performance of buildings participating in demand response (DR) programs is usually evaluated with baseline models, which predict what electric demand would have been if a DR event had not been called. Different baseline models produce different results. Moreover, modelers implementing the same baseline model often make different model implementation choices producing different results. Using real data from a DR program in CA and a regression-based baseline model, which relates building demand to time of week, outdoor air temperature, and building operational mode, we analyze the effect of model implementation choices on DR shed estimates. Results indicate strong sensitivities to the outdoor air temperature data source and bad data filtration methods, with standard deviations of differences in shed estimates of %20-30 kW, and weaker sensitivities to demand/temperature data resolution, data alignment, and methods for determining when buildings are occupied, with standard deviations of differences in shed estimates of %2-5 kW.

Novel Single Group-Based Indirect Customer Baseline Load Calculation Method for Residential Demand Response

“…The regression methods fit a linear/non-linear function to describe the relationship between electricity usage and explanatory variables (e.g., temperature) [13], [14]. For example, SDG&E applies regression to determine electricity usage VOLUME 4, 2016 during an event window based on the weather and the day of the week.…”

“…Most of the existing work focuses on CBL calculation methods for residential customers and operation strategies for DR operators. The CBL calculation methods include direct CBL calculation such as averaging methods [9], regression [13], [14], deep learning [15]- [21], and probabilistic methods [7], [22]- [25] and indirect CBL calculation methods such as control group methods [24], [26]- [35]. The operation strategies for DR operators include optimal bidding strategy [38]- [47] and modeling of residential customers [8], [23], [48]- [53].…”

Demand baseline estimation using similarity‐based technique for tropical and wet climates

“…The third category of BE consists of regression models [7][8][9]. These techniques regress the baseline demand at every time period as the dependent variable, while considering predictors such as calendar variables, weather, and daylight factors.…”