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Title: Non-Routine Event Detection and Adjustment (NRE) v1

Abstract

The non-routine event (NRE) detection and adjustment module provides algorithms to detect and to adjust changes in energy use that are not attributable to installed efficiency measures, and not accounted for in the baseline model's independent variables. The detection algorithm is based on statistical change point algorithm and the adjustment uses a data driven approach to quantify the changes in the energy use that are induced by the NRE.

Developers:
 [1];  [1]
  1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Release Date:
Project Type:
Open Source, Publicly Available Repository
Software Type:
Scientific
Licenses:
BSD 3-clause "New" or "Revised" License
Sponsoring Org.:
USDOE

Primary Award/Contract Number:
AC02-05CH11231
Code ID:
21947
Site Accession Number:
2018-104
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Country of Origin:
United States

Citation Formats

Granderson, Jessica, Touzani, Samir, and USDOE. Non-Routine Event Detection and Adjustment (NRE) v1. Computer software. https://www.osti.gov//servlets/purl/1529273. USDOE. 20 Jul. 2018. Web. doi:10.11578/dc.20181218.9.
Granderson, Jessica, Touzani, Samir, & USDOE. (2018, July 20). Non-Routine Event Detection and Adjustment (NRE) v1 [Computer software]. https://www.osti.gov//servlets/purl/1529273. doi:10.11578/dc.20181218.9.
Granderson, Jessica, Touzani, Samir, and USDOE. Non-Routine Event Detection and Adjustment (NRE) v1. Computer software. July 20, 2018. https://www.osti.gov//servlets/purl/1529273. doi:10.11578/dc.20181218.9.
@misc{osti_1529273,
title = {Non-Routine Event Detection and Adjustment (NRE) v1},
author = {Granderson, Jessica and Touzani, Samir and USDOE},
abstractNote = {The non-routine event (NRE) detection and adjustment module provides algorithms to detect and to adjust changes in energy use that are not attributable to installed efficiency measures, and not accounted for in the baseline model's independent variables. The detection algorithm is based on statistical change point algorithm and the adjustment uses a data driven approach to quantify the changes in the energy use that are induced by the NRE.},
url = {https://www.osti.gov//servlets/purl/1529273},
doi = {10.11578/dc.20181218.9},
year = {2018},
month = {7},
note =
}

Software:
Publicly Accessible Repository
https://github.com/LBNL-ETA/nre

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