Heterogeneous Transfer Learning for Thermal Comfort Modeling

Hu, Weizheng; Luo, Yong; Lu, Zongqing; Wen, Yonggang

doi:10.1145/3360322.3360843

Cited by 22 publications

(13 citation statements)

References 13 publications

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“…[7] for comparison with one of the existing stateof-the-art method in dynamic environment as a baseline. As described in the literature [7], [8], random forest exhibits excellent performance as a machine learning method used for thermal sensation estimation, particularly for small datasets. In this work, we assume the environment where we can measure time-series features by a wristband only.…”

Section: E Comparison With Existing Workmentioning

confidence: 93%

“…Various works have shown that deep learning-based techniques perform better to estimate TSV than other traditional machine learning techniques. Hu et al [8] showed their deep transfer learning-based approach was superior to other machine learning approaches such as Naive Bayes, Support Vector Machine, Decision Tree, Multi-layer perceptron, and k-Nearest Neighbors. However, this study transfers the knowledge trained by open datasets, which do not consider the dynamic situation.…”

Section: Deep Learning-based Approachesmentioning

confidence: 99%

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TSVNet: Combining Time-Series and Opportunistic Sensing by Transfer Learning for Dynamic Thermal Sensation Estimation

2021

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Section: E Comparison With Existing Workmentioning

confidence: 93%

Section: Deep Learning-based Approachesmentioning

confidence: 99%

TSVNet: Combining Time-Series and Opportunistic Sensing by Transfer Learning for Dynamic Thermal Sensation Estimation

2021

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“…Transfer learning algorithms and strategies have their wide application as a prospective solution in the cyberphysical building environment domain. Hu et al (2019) proposed a multilayer neural network-based HTL architecture to decide on personal thermal comfort intelligently. The authors include heterogeneous features in two staged learning architectures.…”

Section: Transfer Learningmentioning

confidence: 99%

Trending machine learning models in cyber‐physical building environment: A survey

Hasan

Roy

2021

WIREs Data Min & Knowl

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Electricity usage of buildings (including offices, malls, and residential apartments) represents a significant portion of a nation's energy expenditure and carbon footprint. In the United States, the buildings' appliances consume 72% of the total produced electricity approximately. In this regard, cyber‐physical system (CPS) researchers have put forth associated research questions to reduce cyber‐physical building environment energy consumption by minimizing the energy dissipation while securing occupants' comfort. Some of the questions in CPS building include finding the optimal HVAC control, monitoring appliances' energy usage, detecting insulation problems, estimating the occupants' number and activities, managing thermal comfort, intelligently interacting with the smart grid. Various machine learning (ML) applications have been studied in recent CPS researches to improve building energy efficiency by addressing these questions. In this paper, we comprehensively review and report on the contemporary applications of ML algorithms such as deep learning, transfer learning, active learning, reinforcement learning, and other emerging techniques that propose and envision to address the above challenges in the CPS building environment. Finally, we conclude this article by discussing diverse existing open questions and prospective future directions in the CPS building environment research.This article is categorized under: Technologies > Machine Learning Technologies > Reinforcement Learning

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“…To deal with the challenges of collecting high-quality data from multiple urban areas, in particular highquality labels or annotated data, approaches such as semi-supervised learning [248], or transfer learning [249] could be used to adapt data-driven models from one city to another. However, for these techniques, or similar black-box AI techniques, to be widely accepted in practice, not just in research domain, future works require models that enable feedback and collaboration across disciplines, incorporating expert views of the models.…”

Section: Modellingmentioning

confidence: 99%

Modelling urban-scale occupant behaviour, mobility, and energy in buildings: A survey

Salim

Dong

Ouf

et al. 2020

Building and Environment

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The proliferation of urban sensing, IoT, and big data in cities provides unprecedented opportunities for a deeper understanding of occupant behaviour and energy usage patterns at the urban scale. This enables data-driven building and energy models to capture the urban dynamics, specifically the intrinsic occupant and energy use behavioural profiles that are not usually considered in traditional models. Although there are related reviews, none investigated urban data for use in modelling occupant behaviour and energy use at multiple scales, from buildings to neighbourhood to city. This survey paper aims to fill this gap by providing a critical summary and analysis of the works reported in the literature. We present the different sources of occupant-centric urban data that are useful for data-driven modelling and categorise the range of applications and recent data-driven modelling techniques for urban behaviour and energy modelling, along with the traditional stochastic and simulation-based approaches. Finally, we present a set of recommendations for future directions in data-driven modelling of occupant behaviour and energy in buildings at the urban scale.

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Heterogeneous Transfer Learning for Thermal Comfort Modeling

Cited by 22 publications

References 13 publications

TSVNet: Combining Time-Series and Opportunistic Sensing by Transfer Learning for Dynamic Thermal Sensation Estimation

TSVNet: Combining Time-Series and Opportunistic Sensing by Transfer Learning for Dynamic Thermal Sensation Estimation

Trending machine learning models in cyber‐physical building environment: A survey

Modelling urban-scale occupant behaviour, mobility, and energy in buildings: A survey

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