Energy Management in an Agile Workspace using AI-driven Forecasting and Anomaly Detection
4th IEEE Global Power, Energy and Communication Conference, GPECOM 2022
; : 644-649, 2022.
Article
in English
| Scopus | ID: covidwho-1973467
ABSTRACT
Smart building technologies transform buildings into agile, sustainable, and health-conscious ecosystems by leveraging IoT platforms. In this regard, we have developed a Persuasive Energy Conscious Network (PECN) at the University of Glasgow to understand the user-centric energy consumption patterns in an agile workspace. PECN consists of desk-level energy monitoring sensors that enable us to develop user-centric models that can be exploited to characterize the normal energy usage behavior of an office occupant. In this study, we make use of staked long short-term memory (LSTM) to forecast future energy demands. Moreover, we employed statistical techniques to automate the detection of anomalous power consumption patterns. Our experimental results indicate that post-anomaly resolution leads to 6.37% improvement in the forecasting accuracy. © 2022 IEEE.
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Databases of international organizations
Database:
Scopus
Language:
English
Journal:
4th IEEE Global Power, Energy and Communication Conference, GPECOM 2022
Year:
2022
Document Type:
Article
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