Exploration of tensor decomposition applied to commercial building baseline estimation Baseline estimation is a critical task for commercial buildings that participate in demand response programs and need to assess the impact of their strategies. The problem is to predict what the power profile would have been had the demand response event not taken place. This paper explores the use of tensor decomposition in baseline estimation. We apply the method to submetered fan power data from demand response experiments that were run to assess a fast demand response strategy expected to primarily impact the fans. Baselining this fan power data is critical for evaluating the results, but doing so presents new challenges not readily addressed by existing techniques designed primarily for baselining whole building electric loads. We find that tensor decomposition of the fan power data identifies components that capture both dominant daily patterns and demand response events, and that are generally more interpretable than those found by principal component analysis. We conclude by discussing how these components and related techniques can aid in developing new baseline models.

Synchronous Pattern Matching PrincipleBased Residential Demand Response Baseline Estimation: Mechanism Analysis and Approach Description Most current customer baseline load (CBL) estimation methods for incentivebased demand response (DR) rely heavily on historical data and are unable to adapt to the cases when the load patterns (LPs) in the DR event day are not similar enough to those in nonDR days. After the error generation mechanism of current methods is revealed, a synchronous pattern matching principlebased residential CBL estimation approach without historical data requirement is proposed. All customers are split into DR and CONTROL group, including DR participants and nonDR customers, respectively. First, all CONTROL group customers are clustered into several nonoverlapping clusters according to LPs similarity in the DR event day. Second, each DR participant is matched to the most similar cluster in the CONTROL group according to the similarity between its load curve segments in DR event day, excluding DR part and cluster centroids. Third, the CBL of each DR participant is estimated with an optimized weight combination method using the load data within the DR event period of all the customers in the very matching cluster in the CONTROL group. A comparison with five wellknown CBL estimation methods using a dataset of 736 residential customers indicates that the proposed approach has better overall performance than other current CBL estimation methods.