Using an ensemble machine learning methodology-Bagging to predict occupants’ thermal comfort in buildings

Wu, Zhibin; Li, Nianping; Peng, Jinqing; Cui, Haijiao; Liu, Penglong; Li, Hongqiang; Li, Xiwang

doi:10.1016/j.enbuild.2018.05.031

Cited by 81 publications

(25 citation statements)

References 39 publications

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“…It also verifies the usefulness of the LPP in supervised physiological feature fusion [1]. Consistent with the results from Wu et al [41], the Bagging-based approach further increase the classification rate by integrating the outputs of multiple weak models. Specifically, EEG features fed into the EL-SDAE adopt comprehensive frequency ranges in the theta, alpha, beta, and gamma bands, which are consistent with most existing work on MW classification using EEG power features.…”

Section: Discussionsupporting

confidence: 86%

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Assessing cognitive mental workload via EEG signals and an ensemble deep learning classifier based on denoising autoencoders

Yang

Yin

Wang

et al. 2019

Computers in Biology and Medicine

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To estimate the reliability and cognitive states of operator performance in a human-machine collaborative environment, we propose a novel human mental workload (MW) recognizer based on deep learning principles and utilizing the features of the electroencephalogram (EEG). To determine personalized properties in high dimensional EEG indicators, we introduce a feature mapping layer in stacked denoising autoencoder (SDAE) that is capable of preserving the local information in EEG dynamics. The ensemble classifier is then built via the subjectspecific integrated deep learning committee, and adapts to the cognitive properties of a specific human operator and alleviates inter-subject feature variations. We validate our algorithms and the ensemble SDAE classifier with local information preservation (denoted by EL-SDAE) on an EEG database collected during the execution of complex human-machine tasks. The classification performance indicates that the EL-SDAE outperforms several classical MW estimators when its optimal network architecture has been identified.

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Section: Discussionsupporting

confidence: 86%

“…To fuse the outputs from all the base learners C t , we implement Bagging approach [41] in the EL-SDAE. The Bagging ensemble framework randomly selects a subset from the entire EEG training data with replacement sampling via the bootstrap method [42].…”

Section: Function: Sdae_trainmentioning

confidence: 99%

Assessing cognitive mental workload via EEG signals and an ensemble deep learning classifier based on denoising autoencoders

Yang

Yin

Wang

et al. 2019

Computers in Biology and Medicine

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“…Bootstrap aggregating (bagging) [72][73][74][75] is one of the primary ensemble methods that uses bootstrap sampling. With bagging, the ensemble, as a homogeneous ensemble, was combined to generate the prediction model with data resampling.…”

Section: Bagging (Bootstrap Aggregating)mentioning

confidence: 99%

Cost-sensitive probability for weighted voting in an ensemble model for multi-class classification problems

Rojarath

Songpan

2021

Appl Intell

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Ensemble learning is an algorithm that utilizes various types of classification models. This algorithm can enhance the prediction efficiency of component models. However, the efficiency of combining models typically depends on the diversity and accuracy of the predicted results of ensemble models. However, the problem of multi-class data is still encountered. In the proposed approach, cost-sensitive learning was implemented to evaluate the prediction accuracy for each class, which was used to construct a cost-sensitivity matrix of the true positive (TP) rate. This TP rate can be used as a weight value and combined with a probability value to drive ensemble learning for a specified class. We proposed an ensemble model, which was a type of heterogenous model, namely, a combination of various individual classification models (support vector machine, Bayes, K-nearest neighbour, naïve Bayes, decision tree, and multi-layer perceptron) in experiments on 3-, 4-, 5- and 6-classifier models. The efficiencies of the propose models were compared to those of the individual classifier model and homogenous models (Adaboost, bagging, stacking, voting, random forest, and random subspaces) with various multi-class data sets. The experimental results demonstrate that the cost-sensitive probability for the weighted voting ensemble model that was derived from 3 models provided the most accurate results for the dataset in multi-class prediction. The objective of this study was to increase the efficiency of predicting classification results in multi-class classification tasks and to improve the classification results.

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“…Wu et al investigated an intelligent ensemble machine learning (EML) method Bagging for thermal perception prediction. They compared Bagging approach with ANN and SVM regarding conventional statistical indicators [18]. Kim et al proposed a personal comfort model to predict individuals' thermal preference by using a new type of feedback, occupants' heating and cooling behaviour with a personal comfort system (PCS) to improve occupant satisfaction and energy use in buildings [19].…”

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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Using an ensemble machine learning methodology-Bagging to predict occupants’ thermal comfort in buildings

Cited by 81 publications

References 39 publications

Assessing cognitive mental workload via EEG signals and an ensemble deep learning classifier based on denoising autoencoders

Assessing cognitive mental workload via EEG signals and an ensemble deep learning classifier based on denoising autoencoders

Cost-sensitive probability for weighted voting in an ensemble model for multi-class classification problems

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

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