[HTML][HTML] Deep and transfer learning for building occupancy detection: A review and comparative analysis
The building internet of things (BIoT) is quite a promising concept for curtailing energy
consumption, reducing costs, and promoting building transformation. Besides, integrating …
consumption, reducing costs, and promoting building transformation. Besides, integrating …
Transfer learning with time series data: a systematic mapping study
Transfer Learning is a well-studied concept in machine learning, that relaxes the assumption
that training and testing data need to be drawn from the same distribution. Recent success in …
that training and testing data need to be drawn from the same distribution. Recent success in …
Semi-supervised federated learning for activity recognition
Training deep learning models on in-home IoT sensory data is commonly used to recognise
human activities. Recently, federated learning systems that use edge devices as clients to …
human activities. Recently, federated learning systems that use edge devices as clients to …
Unsupervised domain adaptation without source data for estimating occupancy and recognizing activities in smart buildings
Abstract Activities Recognition (AR) and Occupancy Estimation (OE) are topics of current
interest. AR and OE help many smart building applications such as energy systems and …
interest. AR and OE help many smart building applications such as energy systems and …
Identifying grey-box thermal models with Bayesian neural networks
Smart thermostats are one of the most prevalent home automation products. Despite the
importance of having an accurate thermal model for the operation of smart thermostats …
importance of having an accurate thermal model for the operation of smart thermostats …
Diversity for transfer in learning-based control of buildings
The application of reinforcement learning to the optimal control of building systems has
gained traction in recent years as it can cut the building energy consumption and improve …
gained traction in recent years as it can cut the building energy consumption and improve …
Day-ahead prediction of plug-in loads using a long short-term memory neural network
The aim of this work is to develop and validate a miscellaneous electric loads (MEL)
predictive model that does not require occupant-wise or building-wise model training nor …
predictive model that does not require occupant-wise or building-wise model training nor …
Adapting wireless mesh network configuration from simulation to reality via deep learning based domain adaptation
Recent years have witnessed the rapid deployments of wireless mesh networks (WMNs) for
industrial automation, military operations, smart energy, etc. Although WMNs work …
industrial automation, military operations, smart energy, etc. Although WMNs work …
Overcoming Data Scarcity through Transfer Learning in CO2-Based Building Occupancy Detection
Knowing indoor occupancy states is crucial for energy optimization in buildings. While
neural networks can effectively be used to detect occupancy based on carbon dioxide …
neural networks can effectively be used to detect occupancy based on carbon dioxide …
Enabling Reliable Environmental Sensing with LoRa, Energy Harvesting, and Domain Adaptation
Environmental sensing is essential for many applications. Many existing efforts rely on the
readings provided by the weather stations maintained by federal, regional, or local …
readings provided by the weather stations maintained by federal, regional, or local …