  This paper presents a novel Iterative Bidirectional Gradient Boosting Model
(IBi-GBM) for estimating the baseline of Conservation Voltage Reduction (CVR)
programs. In contrast to many existing methods, we treat CVR baseline
estimation as a missing data retrieval problem. The approach involves dividing
the load and its corresponding temperature profiles into three periods:
pre-CVR, CVR, and post-CVR. To restore the missing load profile during the CVR
period, the method employs a three-step process. First, a forward-pass GBM is
executed using data from the pre-CVR period as inputs. Subsequently, a
backward-pass GBM is applied using data from the post-CVR period. The two
restored load profiles are reconciled, considering pre-calculated weights
derived from forecasting accuracy, and only the leftmost and rightmost points
are retained. The newly restored points are then included as inputs for the
subsequent iteration. This iterative procedure continues until the original
load data in the CVR period is fully restored. We develop IBi-GBM using actual
smart meter and Supervisory Control and Data Acquisition (SCADA) data. Our
results demonstrate that IBi-GBM exhibits robust performance across various
data resolutions and in different seasons and outperforms existing methods by
achieving a 1-2% reduction in normalized Root Mean Square Error (nRMSE).
