Comparing machine learning algorithms in predicting thermal sensation using ASHRAE Comfort Database II

Luo, Maohui; Xie, Jiaqing; Yan, Yichen; Ke, Zhihao; Yu, Peiran; Wang, Zi; Zhang, Jingsi

doi:10.1016/j.enbuild.2020.109776

Cited by 137 publications

(63 citation statements)

References 36 publications

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“…A decision tree (DT) is a technique based on partitioning the dataset into groups in the form of a flowchart. This technique has been widely used in predicting buildings' energy consumption [14,55] and user comfort indices [47], as well as modeling buildings' energy demands [56].…”

Section: Methodsmentioning

confidence: 99%

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Use of Machine Learning Methods for Indoor Temperature Forecasting

et al. 2021

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Improving the energy efficiency of the building sector has become an increasing concern in the world, given the alarming reports of greenhouse gas emissions. The management of building energy systems is considered an essential means for achieving this goal. Predicting indoor temperature constitutes a critical task for the management strategies of these systems. Several approaches have been developed for predicting indoor temperature. Determining the most effective has thus become a necessity. This paper contributes to this objective by comparing the ability of seven machine learning algorithms (ML) and the thermal gray box model to predict the indoor temperature of a closed room. The comparison was conducted on a set of data recorded in a room of the Laboratory of Civil Engineering and geo-Environment (LGCgE) at Lille University. The results showed that the best prediction was obtained with the artificial neural network (ANN) and extra trees regressor (ET) methods, which outperformed the thermal gray box model.

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Section: Methodsmentioning

confidence: 99%

“…The dataset was divided into two subsets to train and test the chosen algorithms. The 70% and 80% training proportions are most often used in the literature [46][47][48]64,65]. To determine the most appropriate ratios for the dataset, values ranging from 50% to 80% were tested in this study.…”

Section: Methodsmentioning

confidence: 99%

Use of Machine Learning Methods for Indoor Temperature Forecasting

et al. 2021

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“…A random forest classifier from the scikit learn package was chosen to handle this comfort prediction. Random forest classifiers have been proven to have the highest accuracy at predicting personal comfort in one previous study [77] and is one of the best performing of other recent studies [64,65,78]. The decision was made to focus on the implementation of a single model type that has been proven effective and is straightforward to use based on documentation and ease-of-tuning.…”

Section: Occupant Comfort Preference Predictionmentioning

confidence: 99%

“…Model classification results were calculated using the F1-micro scores (as shown in Equation ( 1)) which were equivalent to accuracy in the a multi-class classification problem by calculating precision and recall averaged across all classes, that is, subjective thermal comfort response value. As the objective was to provide a comparison among different feature sets with a standard metric, F1-micro was chosen due to its usage for benchmarking different aspects of the modelling pipeline in thermal comfort datasets [78].…”

mentioning

confidence: 99%

Humans-as-a-Sensor for Buildings—Intensive Longitudinal Indoor Comfort Models

et al. 2020

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Evaluating and optimising human comfort within the built environment is challenging due to the large number of physiological, psychological and environmental variables that affect occupant comfort preference. Human perception could be helpful to capture these disparate phenomena and interpreting their impact; the challenge is collecting spatially and temporally diverse subjective feedback in a scalable way. This paper presents a methodology to collect intensive longitudinal subjective feedback of comfort-based preference using micro ecological momentary assessments on a smartwatch platform. An experiment with 30 occupants over two weeks produced 4378 field-based surveys for thermal, noise, and acoustic preference. The occupants and the spaces in which they left feedback were then clustered according to these preference tendencies. These groups were used to create different feature sets with combinations of environmental and physiological variables, for use in a multi-class classification task. These classification models were trained on a feature set that was developed from time-series attributes, environmental and near-body sensors, heart rate, and the historical preferences of both the individual and the comfort group assigned. The most accurate model had multi-class classification F1 micro scores of 64%, 80% and 86% for thermal, light, and noise preference, respectively. The discussion outlines how these models can enhance comfort preference prediction when supplementing data from installed sensors. The approach presented prompts reflection on how the building analysis community evaluates, controls, and designs indoor environments through balancing the measurement of variables with occupant preferences in an intensive longitudinal way.

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“…Luo et al summarized the recent literature during the period of 2016-2019, from perspectives of predicted comfort indices, algorithms applied, input features, data sources, sample size, training proportion, predicting accuracy, etc. [26].…”

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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Comparing machine learning algorithms in predicting thermal sensation using ASHRAE Comfort Database II

Cited by 137 publications

References 36 publications

Use of Machine Learning Methods for Indoor Temperature Forecasting

Use of Machine Learning Methods for Indoor Temperature Forecasting

Humans-as-a-Sensor for Buildings—Intensive Longitudinal Indoor Comfort Models

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

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