An Iterative Bidirectional Gradient Boosting Approach for CVR Baseline Estimation

TL;DR

This paper introduces IBi-GBM, an iterative bidirectional gradient boosting method that effectively estimates CVR baselines by treating it as a missing data problem, improving accuracy over existing methods.

Contribution

The paper proposes a novel iterative bidirectional gradient boosting approach for CVR baseline estimation, incorporating a missing data framework and iterative reconciliation to enhance accuracy.

Findings

Achieves 1-2% lower nRMSE compared to existing methods.

Robust performance across different data resolutions and seasons.

Demonstrates effectiveness using real smart meter and SCADA data.

Abstract

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).