DeepAI
Log In Sign Up

Role of Deep LSTM Neural Networks And WiFi Networks in Support of Occupancy Prediction in Smart Buildings

11/28/2017
by   Basheer Qolomany, et al.
0

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 data sets 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 reduced by 88.2 levels models and by 80.9

READ FULL TEXT

page 1

page 2

page 3

page 4

07/28/2021

Demand Forecasting in Smart Grid Using Long Short-Term Memory

Demand forecasting in power sector has become an important part of moder...
09/10/2019

LSTM-MSNet: Leveraging Forecasts on Sets of Related Time Series with Multiple Seasonal Patterns

Generating forecasts for time series with multiple seasonal cycles is an...
10/29/2017

Predicting Floor-Level for 911 Calls with Recurrent Neural Networks and Smartphone Sensor Data

In cities with tall buildings, emergency responders need accurate floor-...
06/26/2021

Short-Term Load Forecasting for Smart HomeAppliances with Sequence to Sequence Learning

Appliance-level load forecasting plays a critical role in residential en...
10/16/2020

Flexible, Decentralized Access Control for Smart Buildings with Smart Contracts

Large commercial buildings are complex cyber-physical systems containing...
08/05/2022

Lisbon Hotspots: Wi-Fi access point dataset for time-bound location proofs

Wi-Fi hotspots are a valuable resource for people on the go, especially ...