Power Laws: Detecting Anomalies in Usage Hosted By Schneider Electric
Commercial buildings waste an estimated 15% to 30% of energy used due to poorly maintained, degraded, and improperly controlled equipment. Competitors built quick-response algorithms to find anomalies in energy use and elevate them for human attention and intervention.
No. of Entries
PINGANAI_1st Place Team
Each challenge in the Power Laws series explored a different aspect of energy efficiency and management. The winning algorithms were released under an open source license to spread understanding about how energy modeling works and what approaches are most effective.
As energy consumption of buildings has steadily increased, more and more building do not perform as intended by their designers. Typical buildings consume around 20% more energy than necessary due to faulty construction, malfunctioning equipment, incorrectly configured control systems, and inappropriate operating procedures.
Automatic, quick-responding, accurate, and reliable fault detection can ensure better operations and save energy. In this competition, data scientists all over the world built algorithms to identify anomalous energy consumption.
Identifying anomalies was a tricky task, and the best performers combined human judgment with machine suggestions. The winning model was very precise in its predictions (which was highly weighted in this challenge), correctly identifying anomalies in 100% of the observations it predicted to be anomalous.
Ultimately, these ideas can help drive forward the kinds of anomalies that we hope to identify in building energy use and to respond more quickly to energy-wasting defects.
RESULTS ANNOUNCEMENT + MEET THE WINNERS
Power Laws: Detecting Anomalies in Usage: Rules and Terms of Data Use
DrivenData Competition Rules
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Private sharing of code
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Public dissemination of entries
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Open licensing of winners
Winning solutions need to be made available under The MIT License (an open source software license commonly described at http://opensource.org/licenses/MIT) in order to be eligible for recognition and prize money if offered.
Documentation for winning solutions
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External data is not allowed unless otherwise noted explicitly on these competition pages. Participants agree to make no attempt to use additional data or data sources not provided on these competition pages.
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Data use and code sharing
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