A Unified Methodology to Predict Wi-Fi Network Usage in Smart Buildings

Apostolo, Guilherme Henrique; Bernardini, Flávia; Magalhães, Luiz; Muchaluat-Saade, Débora C.

doi:10.1109/access.2020.3048891

Cited by 10 publications

(4 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A campus-scale occupancy prediction framework for HVAC scheduling was proposed by Trivedi et al where they trained WiFi-derived occupancy counts from 112 campus buildings on an ensemble gradient boosting regressor and achieved 95.35% model accuracy. Their large dataset captured a stronger spatio-temporal variation in occupancy compared to other studies that investigated fewer buildings [28,26]. However, they only reported the missed and waste time from using static schedules over their proposed WiFiderived schedules but did not provide an estimate on actual energy savings.…”

Section: Wifi-sensed Occupant Information For Hvac Schedulingmentioning

confidence: 98%

“…[27] compared the estimated occupancy using CO 2 sensorS to WiFi networks and found that WiFi networks provided higher accuracy and had lower implementation cost. [28] explored supervised classification and regression models in predicting both occupant presence and counts respectively in an academic building and achieved up to 86.69% accuracy and 0.29 Root Mean Square Percentage Error. The authors in [26] sought to develop regression models for 24 hours in advance occupant count prediction using Multi-Layer Receptor (MLR) and Artificial Neural Network (ANN) supervised regression models and achieved 83.1% and 90.1% accuracy on the test sets of the MLR and ANN models respectively.…”

Section: Occupant Information Sensingmentioning

confidence: 99%

See 1 more Smart Citation

MARTINI: Smart Meter Driven Estimation of HVAC Schedules and Energy Savings Based on WiFi Sensing and Clustering

Nweye,

Nagy

2021

Preprint

View full text Add to dashboard Cite

MARTINI derives ramp-up and ramp-down times of HVAC systems based on measured occupancy schedules • Savings estimates are provided, even if no occupancy data is available • Daily and seasonal occupancy and chilled water profile identified respectively.• 8.1%-10.8% (summer) and 0.2%-5.9% (fall) chilled water average energy savings for UT campus • Saving estimates are within 0.9-2.4% projections of building energy performance simulation.

show abstract

Section: Wifi-sensed Occupant Information For Hvac Schedulingmentioning

confidence: 98%

Section: Occupant Information Sensingmentioning

confidence: 99%

MARTINI: Smart Meter Driven Estimation of HVAC Schedules and Energy Savings Based on WiFi Sensing and Clustering

Nweye,

Nagy

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Operation, Management and Self-deployment. AI-based RAN management solutions can be found from both academia and industry perspectives, dealing with aspects related to ZTM management of small cells and APs, including activation and user association, self-deployment, and optimized configuration [150]- [157].…”

Section: Playgrounds For Building and Testingmentioning

confidence: 99%

“…In particular, in [156], the aforementioned measurements are used in the context of cognitive networking to estimate performance indicators that are then fed on a semi-supervised SVM to create location heat-map. Conversely, the authors of [157] present a set of classification and regression models to predict the utilization of the APs in a Wi-Fi network and proactively activate/deactivate the APs to reduce the energy consumed. Similar to [138], the dataset is collected using off-the-shelf APs running OpenWRT.…”

Section: Playgrounds For Building and Testingmentioning

confidence: 99%

Zero Touch Management: A Survey of Network Automation Solutions for 5G and 6G Networks

Coronado

Behravesh

Subramanya

et al. 2022

IEEE Commun. Surv. Tutorials

View full text Add to dashboard Cite

Mobile networks are facing an unprecedented demand for high-speed connectivity originating from novel mobile applications and services and, in general, from the adoption curve of mobile devices. However, coping with the service requirements imposed by current and future applications and services is very difficult since mobile networks are becoming progressively more heterogeneous and more complex. In this context, a promising approach is the adoption of novel network automation solutions and, in particular, of zero-touch management techniques. In this work, we refer to zero-touch management as a fully autonomous network management solution with human oversight. This survey sits at the crossroad between zero-touch management and mobile and wireless network research, effectively bridging a gap in terms of literature review between the two domains. In this paper, we first provide a taxonomy of network management solutions. We then discuss the relevant state-of-the-art on autonomous mobile networks. The concept of zero-touch management and the associated standardization efforts are then introduced. The survey continues with a review of the most important technological enablers for zero-touch management. The network automation solutions from the RAN to the core network, including end-toend aspects such as security, are then surveyed. Finally, we close this article with the current challenges and research directions.

show abstract

MARTINI: Smart meter driven estimation of HVAC schedules and energy savings based on Wi-Fi sensing and clustering

Nweye

Nagy

2022

Applied Energy

View full text Add to dashboard Cite

A Unified Methodology to Predict Wi-Fi Network Usage in Smart Buildings

Cited by 10 publications

References 31 publications

MARTINI: Smart Meter Driven Estimation of HVAC Schedules and Energy Savings Based on WiFi Sensing and Clustering

MARTINI: Smart Meter Driven Estimation of HVAC Schedules and Energy Savings Based on WiFi Sensing and Clustering

Zero Touch Management: A Survey of Network Automation Solutions for 5G and 6G Networks

MARTINI: Smart meter driven estimation of HVAC schedules and energy savings based on Wi-Fi sensing and clustering

Contact Info

Product

Resources

About