Detection and evaluation of heating load of building by machine learning

Swhli, Khaled Mohamed Himair; Jović, Srđan; Arsić, Nebojša; Spalević, Petar

doi:10.1108/sr-07-2017-0139

Cited by 5 publications

(3 citation statements)

References 13 publications

(12 reference statements)

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“…Environmental monitoring management system, also known as environmental monitoring information management system, is an information system that manages a large amount of environmental monitoring information and data storage with computer technology and database technology as the core. e environmental monitoring system can be divided into four modules according to its functions: data acquisition subsystem, data transmission subsystem, monitoring center server, and remote monitoring application [8,9].…”

Section: Atmospheric Environment Monitoring Systemmentioning

confidence: 99%

Online Learning Platform for Air Environment Detection and Career Planning Based on 5G Network

Ning

2022

Mobile Information Systems

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With the faster and faster economic and social development, environmental problems will gradually become prominent. At the same time, mobile learning is receiving more and more attention from trainers, and mobile learning is bound to become an important and effective means for corporate training. Based on the 5G network, this paper conducts research and analysis on the air environment detection and career planning online learning platform. This article first introduces the atmospheric environment monitoring system based on 5G wireless sensor network, analyzes the gray GM(1, 1) model modeling to better predict air quality, and gives the model programming the basic idea. Gray system theory model, also known as gray model or gray dynamic model, is referred to as GM model. The most typical of them is the gray model GM(1, 1). Subsequently, the air environment quality in City A was tested and predicted, and at the same time, the overall status of the career planning of college students was analyzed and discussed. The results of the study show that, in the forecast of the air quality in City A, it is found that the air quality in City A from 2021 to 2025 is still not optimistic. Among them, the annual average value of PM10 shows a downward trend; the annual average value of SO2 fluctuates greatly, and the annual average value of NO2 is on the rise compared with 2020 and will reach 0.045 mg/m3 in 2025. Therefore, City A still needs to take practical measures to control air quality. The results of the analysis of the overall status of career planning show that in the process of cultivating career planning ability and stimulating learning motivation, it is also necessary to pay attention to the cultivation of social responsibility and dedication.

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Section: Atmospheric Environment Monitoring Systemmentioning

confidence: 99%

Online Learning Platform for Air Environment Detection and Career Planning Based on 5G Network

Ning

2022

Mobile Information Systems

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“…With the expansion of heating scale and the growing complexity of heating data, there is a growing demand for processing the real-time data series of heat supply and predicting the short-term trend accurately for different heating districts and independent buildings [14][15][16][17][18][19][20]. The single prediction model with specific mathematical assumptions and applicable conditions can hardly meet the strict mathematical preconditions and hypotheses concerning the load trend prediction of actual heat supply projects in the background of big data [21][22][23][24]. To realize refined planning and decisionmaking of energy heat supply, the combinatory prediction method came into being, providing an effective way to elevate the accuracy of trend prediction.…”

Section: Introductionmentioning

confidence: 99%

Application of Optimal Combinatory Mathematics Model in Heat Load Trend Prediction

Liu¹,

Liu²,

Jiang³

et al. 2022

IJHT

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The accurate prediction of the short-term heat load trend of buildings helps to prevent energy waste. Combinatory prediction methods need to be studied, as the processing of real-time data series of heat supply and the accurate prediction of short-term trend are more and more demanded by different heating districts and independent buildings. Therefore, this study explores the application of optimal combinatory mathematics model in heat load trend prediction. Firstly, feature extraction was performed on the historical weather data at the locality of the building and the historical data on heat load, creating a short-term trend prediction dataset with days as the unit, and an ultra-short-term trend prediction dataset with hours as the unit. On this basis, a combinatory mathematics model was created for heat load trend prediction. Furthermore, the authors detailed the principles of the two methods, namely, extreme gradient boosting tree (EGBT) and support vector regression (SVR), and explains the combination pattern of the single models in the combinatory model. Then, the weights were optimized by the simulated annealing (SA) algorithm, and the steps of the combinatory model were presented. The proposed model was proved effective through experiments.

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“…Many studies have used machine learning to increase the classification time used to detect and diagnose, such as machine learning based on the induction of autism models [9], machine learning used to detect damage under operational and environmental variability [14], machine learning used to detect and evaluate heating loads on buildings [15], machine learning to detect false opinions on comments [16], machine learning implemented to detect the impact of turbine blades on wildlife and the environment [17], detect traffic jams automatically through sensors wireless traffic using the machine learning approach [18]. In the process of diagnosis or identification includes predictions whether included or not, in determining the class (classification) can use supervised learning.…”

Section: Introductionmentioning

confidence: 99%

Improving The Accuracy of Student Problem Identification Using Rule-Based Machine Learning

2020

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Adolescence are a period of development that is vulnerable to problems and often makes teens unable to control emotions. No exception for adolescents who are studying high school. Problems that do not need to be resolved immediately and bigger problems will arise later on. Many methods of solving students' problems are carried out in a conventional manner which takes time and costly. Therefore, teacher guidance and career guidance at school use the problem checklist method to identify student problems. One thing that promises to improve accuracy with time to identify problems by building information systems using intelligent technology such as machine learning. Machine learning offers sophisticated techniques built by automatic classification that can be utilized by students and teachers to improve accuracy and efficiency in identification. This article discusses issues related to problems faced by senior high school students and proposes a knowledge-based users (rules) machine learning to match the problems and alternative solutions. This system can be used by school counsellors to help students solving their problems and the students to access themselves without having to meet the school counsellor. The results of this research indicate that information system developed based on rule-based machine learning offer a student problem identification which is more accurate, faster, can be done anytime and anywhere, and requires less cost compared to existing conventional methods. Analysis of machine learning with rule-based models using WEKA gives 100% accuracy.

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Detection and evaluation of heating load of building by machine learning

Cited by 5 publications

References 13 publications

Online Learning Platform for Air Environment Detection and Career Planning Based on 5G Network

Online Learning Platform for Air Environment Detection and Career Planning Based on 5G Network

Application of Optimal Combinatory Mathematics Model in Heat Load Trend Prediction

Improving The Accuracy of Student Problem Identification Using Rule-Based Machine Learning

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