Role of Deep LSTM Neural Networks and Wi-Fi Networks in Support of Occupancy Prediction in Smart Buildings

Basheer Qolomany, Ala Al-Fuqaha, Driss Benhaddou, Ajay Gupta

Research output: Chapter in Book/Report/Conference proceedingConference contribution

17 Scopus citations

Abstract

Knowing how many people occupy a building, and where they are located, is a key component of smart building services. Commercial, industrial and residential buildings often incorporate systems used to determine occupancy. However, relatively simple sensor technology and control algorithms limit the effectiveness of smart building services. In this paper we propose to replace sensor technology with time series models that can predict the number of occupants at a given location and time. We use Wi-Fi datasets readily available in abundance for smart building services and train Auto Regression Integrating Moving Average (ARIMA) models and Long Short-Term Memory (LSTM) time series models. As a use case scenario of smart building services, these models allow forecasting of the number of people at a given time and location in 15, 30 and 60 minutes time intervals at building as well as Access Point (AP) level. For LSTM, we build our models in two ways: a separate model for every time scale, and a combined model for the three time scales. Our experiments show that LSTM combined model reduced the computational resources with respect to the number of neurons by 74.48 % for the AP level, and by 67.13 % for the building level. Further, the root mean square error (RMSE) was reduced by 88.2%-93.4% for LSTM in comparison to ARIMA for the building levels models and by 80.9 %-87% for the AP level models.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE 19th Intl Conference on High Performance Computing and Communications, HPCC 2017, 2017 IEEE 15th Intl Conference on Smart City, SmartCity 2017 and 2017 IEEE 3rd Intl Conference on Data Science and Systems, DSS 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages50-57
Number of pages8
ISBN (Electronic)9781538625880
DOIs
StatePublished - Feb 14 2018
Externally publishedYes
Event19th IEEE Intl Conference on High Performance Computing and Communications, 15th IEEE Intl Conference on Smart City, and 3rd IEEE Intl Conference on Data Science and Systems, HPCC/SmartCity/DSS 2017 - Bangkok, Thailand
Duration: Dec 18 2017Dec 20 2017

Publication series

NameProceedings - 2017 IEEE 19th Intl Conference on High Performance Computing and Communications, HPCC 2017, 2017 IEEE 15th Intl Conference on Smart City, SmartCity 2017 and 2017 IEEE 3rd Intl Conference on Data Science and Systems, DSS 2017
Volume2018-January

Conference

Conference19th IEEE Intl Conference on High Performance Computing and Communications, 15th IEEE Intl Conference on Smart City, and 3rd IEEE Intl Conference on Data Science and Systems, HPCC/SmartCity/DSS 2017
Country/TerritoryThailand
CityBangkok
Period12/18/1712/20/17

Keywords

  • ARIMA
  • IoT services
  • LSTM
  • Machine Learning
  • Smart Buildings
  • Smart Homes
  • Time series
  • Wi-Fi networks

ASJC Scopus subject areas

  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications
  • Computational Theory and Mathematics
  • Software

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