Data-Driven Thermal Comfort Prediction With Support Vector Machine

Peng, Bo; Hsieh, Sheng‐Jen

doi:10.1115/msec2017-3003

Cited by 13 publications

(11 citation statements)

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“…To address the first two characteristics of personal comfort models noted above, the review only includes studies that focus on individual occupants as a unit of analysis, and use human feedback in the model development. This effectively excludes 1) studies that adopt a data-driven approach to modeling, but predict thermal comfort of general populations rather than individual occupants [18][19][20][21][22][23], and 2) studies that use synthetic data instead of real-world data to model individuals' thermal comfort [24][25][26]. Table 1 summarizes the findings from this literature review.…”

Section: Review Of Current State Of Researchmentioning

confidence: 99%

Personal comfort models – A new paradigm in thermal comfort for occupant-centric environmental control

Kim

Schiavon

Brager

2018

Building and Environment

361

147

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A personal comfort model is a new approach to thermal comfort modeling that predicts an individual's thermal comfort response, instead of the average response of a large population. It leverages the Internet of Things and machine learning to learn individuals' comfort requirements directly from the data collected in their everyday environment. Its results could be aggregated to predict comfort of a population. To provide guidance on future efforts in this emerging research area, this paper presents a unified framework for personal comfort models. We first define the problem by providing a brief discussion of existing thermal comfort models and their limitations for real-world applications, and then review the current state of research on personal comfort models including a summary of key advances and gaps. We then describe a modeling framework to establish fundamental concepts and methodologies for developing and evaluating personal comfort models, followed by a discussion of how such models can be integrated into indoor environmental controls. Lastly, we discuss the challenges and opportunities for applications of personal comfort models for building design, control, standards, and future research.

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Section: Review Of Current State Of Researchmentioning

confidence: 99%

Personal comfort models – A new paradigm in thermal comfort for occupant-centric environmental control

Kim

Schiavon

Brager

2018

Building and Environment

361

147

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“…Peng and Hsieh developed a platform for simulating occupants' thermal sensations and applied it to examine the performance of SVM on thermal comfort prediction. They also proposed a hybrid SVM-LDA thermal comfort classifier to improve the efficiency of model training [22]. Zhang et al applied ML to control indoor thermal comfort directly with high accuracy to achieve smart building control and operation [23].…”

Section: Machine Learning (Ml) Algorithmsmentioning

confidence: 99%

Intelligent Thermal Comfort Controlling System for Buildings Based on IoT and AI

Zhao

Genovese

2020

Future Internet

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With the improvement of technologies, people's demand for intelligent devices of indoor and outdoor living environments keeps increasing. However, the traditional control system only adjusts living parameters mechanically, which cannot better meet the requirements of human comfort intelligently. This article proposes a building intelligent thermal comfort control system based on the Internet of Things and intelligent artificial intelligence. Through the literature review, various algorithms and prediction methods are analyzed and compared. The system can automatically complete a series of operations through IoT hardware devices which are located at multiple locations in the building with key modules. The code is developed and debugged by Python to establish a model for energy consumption prediction with environmental factors such as temperature, humidity, radiant temperature, and air velocity on thermal comfort indicators. By using the simulation experiments, 1700 data sets are used for training. Then, the output PMV predicted values are compared with the real figure. The results show that the performance of this system is superior to traditional control on energy-saving and comfort.Future Internet 2020, 12, 30 2 of 18 prediction, and the error is relatively large. In recent years, the development trends of computer science, applied mathematics, statistics, and semiconductor hardware have brought iterative progress in artificial intelligence technology. This makes the simulation, analysis, prediction. Literature ReviewBig data mainly refers to data management on a certain scale. Due to the increase of the data volume, speed, and type, traditional methods cannot be used to proceed [1]. The artificial intelligence technology represented by machine learning and deep learning algorithms has a great dependence on datasets. Only when the original data is large enough can the trained algorithm be more accurate. Therefore, before applying the model, it is necessary to obtain enough data for repeated training.In the traditional building with related planning and landscape fields, big data was firstly applied to Geographic Information System (GIS), including a variety of spatial information such as land conditions, terrain, and climate. As for the single building, its automated development is mainly reflected in the field of intelligent building, with small but higher accuracy. The complexity of intelligent building systems is high. Meanwhile, the physical structure, indoor environment, and operating systems of the entire building are monitored and analyzed. It can develop the building in the direction of intelligence. With the help of building big data system, it can analyze the use of the building in real-time, integrate the related information of the surroundings, and let the building make optimal adjustments according to the actual situation [2]. In the process of optimal adjustment, IoT sensors and controllers are usually targeted in the building. The organic combination of physical devices and computer algorithms pla...

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“…With the development of machine learning (ML) and computer vision (CV), some thermal comfort perception methods based on ML and CV were proposed. Support Vector Machine (SVM) are often used for analyzing existing databases (RP-884) and captured environmental parameters [24,[32][33]. Further, Peng [26] use unsupervised and supervised learning to predict occupants' behavior, applied to three types of offices which are single person offices, multi-person offices, and meeting rooms.…”

Section: Related Workmentioning

confidence: 99%

“…All these methods are meaningful attempts. However, due to the challenges of measuring thermal comfort which are (1) skin subtleness variation [24], (2) inter-individual differences [14,25] and (3) temporal intra-individual differences [14,26], there is still no satisfactory method for perceiving human thermal comfort.…”

Section: Introductionmentioning

confidence: 99%

A Contactless Measuring Method of Skin Temperature based on the Skin Sensitivity Index and Deep Learning

et al. 2019

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Featured Application: The NISDL method proposed in this paper can be used for real time noninvasively measuring human skin temperature, which reflect human body thermal comfort status and can be used for control HVAC devices.Abstract: In human-centered intelligent building, real-time measurements of human thermal comfort play critical roles and supply feedback control signals for building heating, ventilation, and air conditioning (HVAC) systems. Due to the challenges of intra-and inter-individual differences and skin subtleness variations, there is no satisfactory solution for thermal comfort measurements until now. In this paper, a non-invasive measuring method based on skin sensitivity index and deep learning (NISDL) was proposed to measure real-time skin temperature. A new evaluating index, named skin sensitivity index (SSI), was defined to overcome individual differences and skin subtleness variations. To illustrate the effectiveness of SSI proposed, two multi-layers deep learning framework (NISDL method I and II) was designed and the DenseNet201 was used for extracting features from skin images. The partly personal saturation temperature (NIPST) algorithm was use for algorithm comparisons. Another deep learning algorithm without SSI (DL) was also generated for algorithm comparisons. Finally, a total of 1.44 million image data was used for algorithm validation. The results show that 55.6180% and 52.2472% error values (NISDL method I, II) are scattered at [0 °C, 0.25 °C), and the same error intervals distribution of NIPST is 35.3933%.

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Data-Driven Thermal Comfort Prediction With Support Vector Machine

Cited by 13 publications

References 0 publications

Personal comfort models – A new paradigm in thermal comfort for occupant-centric environmental control

Personal comfort models – A new paradigm in thermal comfort for occupant-centric environmental control

Intelligent Thermal Comfort Controlling System for Buildings Based on IoT and AI

A Contactless Measuring Method of Skin Temperature based on the Skin Sensitivity Index and Deep Learning

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