@inproceedings{d984c50d000145a39038cc84e5a3136f,
title = "Energy Management in an Agile Workspace using AI-driven Forecasting and Anomaly Detection",
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.",
keywords = "Agile workplace, COVID-19, LSTM, Short term load forecasting, Time series forecasting",
author = "Manzoor, \{Habib Ullah\} and Khan, \{Ahsan Raza\} and Mohammad Al-Quraan and Lina Mohjazi and Ahmad Taha and Hasan Abbas and Sajjad Hussain and Imran, \{Muhammad Ali\} and Ahmed Zoha",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 4th IEEE Global Power, Energy and Communication Conference, GPECOM 2022 ; Conference date: 14-06-2022 Through 17-06-2022",
year = "2022",
doi = "10.1109/GPECOM55404.2022.9815599",
language = "English",
series = "Proceedings - 2022 IEEE 4th Global Power, Energy and Communication Conference, GPECOM 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "644--649",
booktitle = "Proceedings - 2022 IEEE 4th Global Power, Energy and Communication Conference, GPECOM 2022",
address = "United States",
}