Statistical change detection of building energy consumption: Applications to savings estimation

Statistical change detection of building energy consumption: Applications to savings estimation

TitleStatistical change detection of building energy consumption: Applications to savings estimation
Publication TypeJournal Article
Year of Publication2019
AuthorsSamir Touzani, Baptiste Ravache, Eliot Crowe, Jessica Granderson
JournalEnergy and Buildings
Volume185
Pagination123 - 136
Date Published01/2019
ISSN03787788
Abstract

The surge in interval meter data availability and associated activity in energy data analytics has inspired new interest in advanced methods for building efficiency savings estimation. Statistical and machine learning approaches are being explored to improve the energy baseline models used to measure and verify savings. One outstanding challenge is the ability to identify and account for operational changes that may confound savings estimates. In the measurement and verification (M&V) context, 'non-routine events' (NREs) cause changes in building energy use that are not attributable to installed efficiency measures, and not accounted for in the baseline model's independent variables. In the M&V process NREs must be accounted for as 'adjustments' to appropriately attribute the estimated energy savings to the specific efficiency interventions that were implemented. Currently this is a manual and custom process, conducted using professional judgment and engineering expertise. As such it remains a barrier in scaling and standardizing meter-based savings estimation.In this work, a data driven methodology was developed to (partially) automate, and therefore streamline the process of detecting NREs in the post-retrofit period and making associated savings adjustments. The proposed NRE detection algorithm is based on a statistical change point detection method and a dissimilarity metric. The dissimilarity metric measures the proximity between the actual time series of the post-retrofit energy consumption and the projected baseline, which is generated using a statistical baseline model. The suggested approach for NRE adjustment involves the NRE detection algorithm, the M&V practitioner, and a regression modeling algorithm. The performance of the detection and adjustment algorithm was evaluated using a simulation-generated test data set, and two benchmark algorithms. Results show a high true positive detection rate (75%-100% across the test cases), higher than ideal false positive detection rates (20%-70%), and low errors in energy adjustment (<0.7%). These results indicate that the algorithm holds for helping M&V practitioners to streamline the process of handling NREs. Moreover, the change point algorithm and underlying statistical principles could prove valuable for other building analytics applications such as anomaly detection and fault diagnostics.

URLhttps://linkinghub.elsevier.com/retrieve/pii/S0378778818323259
DOI10.1016/j.enbuild.2018.12.020
Short TitleEnergy and Buildings