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A comprehensive comparative analysis of machine learning models for predicting heating and cooling loads
2020
Decision Science Letters
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The present study introduces a set of machine learning-based models to predict the heating and cooling loads in buildings. ...
This includes back-propagation artificial neural network, generalized regression neural network, radial basis neural network, radial kernel support vector machines and ANOVA kernel support vector machines ...
kernel support vector machines. ...
doi:10.5267/j.dsl.2020.3.004
fatcat:jjhy3pi6irf6dgj5xfsw2a3ic4
Prediction of heating load fluctuation based on fuzzy information granulation and support vector machine
2020
Thermal Science
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Then the support vector machine regression prediction model was used for the prediction of the granulation data, finally, the heating load of a district heating system is simulated and verified. ...
In order to achieve the real sense of heating on demand, based on historical heating load data, first of all, the heating load time series data was dealing with fuzzy information granulation, and then ...
Acknowledgments This research was supported by Guangdong electrical and intelligent control engineering technology research center. ...
doi:10.2298/tsci200529307w
fatcat:lgu7kyhgfjfo5n7zl24aka3t24
Weldability Prediction of AHSS Stackups Using Support Vector Machines
2014
International Journal of Computer and Electrical Engineering
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The experiment result showed that Support Vector Machines has a positive and better predict result compared to ANN. The predict accuracy of SVM is 92% while ANN shows 88.9%. ...
This research aims to exploit the ability of prediction by using Support Vector Machines (SVM) and comparing to Artificial Neural Network (ANN) model. ...
Recently, Support Vector Machines (SVM) has been found as one of the most effective approaches for prediction. ...
doi:10.7763/ijcee.2014.v6.823
fatcat:ba4xeooawfgzzmrivirifyn4ta
Modeling surface roughness of point robot laser hardening, with emphasis on the surface
2021
Politehnika
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For predicting the surface roughness of the hardened specimens, the support vector machine and multiple regression is used. ...
The aim of this paper is to present modeling roughness of point robot laser hardened specimens with different parameters of robot laser cell. ...
Support Vector Machine (SVM) is a machine learning model based on statistical learning theory. ...
doi:10.36978/cte.5.1.1
fatcat:ugb4xknervcl3hk4fizbdzukum
State identification of large diameter wet steam pipeline in nuclear power conventional island
2022
E3S Web of Conferences
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In order to analyze the state of pipeline more accurately, two recognition algorithms of support vector machine (SVM) and SOM neural network are proposed. ...
effect on pipeline state recognition, and the effectiveness of this method on pipeline state recognition is verified. ...
The results show that the classification effect of support vector machine without parameter optimization is significantly worse than that of support vector machine after parameter optimization. ...
doi:10.1051/e3sconf/202236001076
fatcat:qm6rwny6gnchvi6q2wgnd7lhui
Experimental Study on Dynamic Simulation for Biofouling Resistance Prediction by Least Squares Support Vector Machine
2012
Energy Procedia
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, fouling resistance as the output variable, found a prediction model of cooling water fouling resistance based on Least Squares Support Vector Machine(LS-SVM). ...
In consideration of influencing factors of biofouling in the cooling water system, a new method of dynamic analog test of cooling water biofouling resistance for shell-tube heat exchanger was presented ...
A prediction model of fouling resistances was established respectively by the least squares support vector machine (LS-SVM) and the BP neural network. ...
doi:10.1016/j.egypro.2012.02.065
fatcat:t45prrhzwfd73omecc4qdzshxe
Real-Time Lime Quality Control through Process Automation
2021
Regular Issue
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To predict, control and enhance the quality of lime during lime making process, five machine-learning-based models such as multivariate linear regression, support vector machine, decision tree, random ...
After the comparison, results show that the model incorporating support vector machine algorithm has least value of root mean square error of 1.23 in predicting future lime quality. ...
ACKNOWLEGEMENT The authors are highly grateful to the International Journal of Emerging Science and Engineering for supporting this research work. ...
doi:10.35940/ijese.b2502.057221
fatcat:vly63whfvzhqtbks5rv343w2vy
Supervised machine learning application of lithofacies classification for a hydrodynamically complex gas - condensate reservoir in Nam Con Son basin
2022
Petrovietnam Journal
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offset top and base of reservoir were orbitally extracted on 4 wells to create the datasets. ...
From the perspective of classification, the random forest method achieved the highest accuracy score of 0.907 compared to support vector machine (0.896), K-nearest neighbours (0.895), and decision tree ...
Acknowledgment The research work described herein was part of Research Project 077.2021.CNKK.QG/HDKHCN, Order 196/ QD-BCT of the Vietnam Ministry of Industry and Trade. ...
doi:10.47800/pvj.2022.06-03
fatcat:m7vfjpopnffthdcmznksboklqm
Machine-Learning-Based Coefficient of Performance Prediction Model for Heat Pump Systems
2021
Applied Sciences
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In this study, the performance prediction model of the air-cooled heat pump system was developed and verified using artificial neural network, support vector machine, random forest, and K-nearest neighbor ...
The operation data of the heat pump system installed in the university laboratory was measured and a prediction model for each machine-learning stage was developed. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/app12010362
fatcat:26ca4c5smbeqroxne6x23pu6da
Support vector machine for the prediction of heating energy use
2018
Thermal Science
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In this paper the daily district heating use of one university campus was predicted using the support vector machine model. ...
Support vector machine (SVM) is the artificial intelligence method that is nowadays gaining popularity. ...
Acknowledgment Data used for this paper were gathered during the study visit to NTNU, as a part of the collaborative project Sustainable Energy and Environment in Western Balkans. ...
doi:10.2298/tsci170526126s
fatcat:envqjz6swfecrap2qihlq3x4bm
Improvement of Proximal Support Vector Machine and Its Application in a New Method of Making a Mixed Refrigerant in the Ground Source Heat Pump System
2014
Sensors & Transducers
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To solve the problem of the refrigerant performance in a ground source heat pump system, the existing proximal support vector machine 1 has been improved and updated into a Weighted Proximal Support Vector ...
Machine (PSVM) model. ...
+ − (19) The second one is the weighted reasoning Support Vector Machine model, the third one is the weighted standard Support Vector Machine model [8] , the corresponding parameters shall be ...
doaj:1f1d1c29c13945c485190fe12ac2ac68
fatcat:a3tm2ydvzfbw5hwsftt2dbyo2i
Uma comparação de técnicas de aprendizado de máquina para a previsão de cargas energéticas em edifícios
2017
Ambiente Construído
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A base de dados do treinamento consiste de oito variáveis de entrada e duas variáveis de saída, todas derivadas de projetos de edifícios. ...
Esses métodos requerem uma fase de treinamento que considera uma base de dados construída a partir de variáveis selecionadas no domínio do problema. ...
Acknowledgments This research was supported by the following Brazilian agencies CNPq (grant 305099/2014-0), CAPES and FAPEMIG (grants TEC APQ 01606/15 and TEC PPM 388/14). ...
doi:10.1590/s1678-86212017000300165
fatcat:5ygylgwylfbnpkkt5cf4ztiuc4
Heat input prediction during dissimilar welding of steel using different machine learning model
2022
International journal of enhanced research in science technology and engineering
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In the current research work, different machine learning model such as random forest (RF), support vector regression (SVR), XGBoost and Linear regression (LR) is used to predict the heat input involved ...
The acceptability of different model is checked based on coefficient of correlation (CC)and root mean square error (RMSE). ...
[5, 6, 7, 8] .A team of researcher [10] developed a hybrid model combining support vector machine (SVM) and relevance vector machine (RVM) to predict the bead geometry during gas metal arc welding of ...
doi:10.55948/ijerste.2022.1115
fatcat:rxk5pvpxonhpnigxsmjkcbtmja
Prediction Method of Wax Deposition Rate in Crude Oil Pipeline Based on RBF Neural Network and Support Vector Machine
2021
E3S Web of Conferences
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Because support vector machine can model and calculate finite samples, it is found that the accuracy of support vector machine is higher. ...
In order to study the complex wax deposition on the pipe wall and calculate the wax deposition under other conditions, this paper uses RBF neural network and support vector machine to predict the wax deposition ...
Acknowledgments This paper is one of the periodical achievements of Dezhou's Key Laboratory of High-efficiency Heat Pump Air Conditioning Equipment and System Energy Saving Technology (Item Number:26). ...
doi:10.1051/e3sconf/202127104007
fatcat:ndkeoygvdvdd7jzl6jnmlelgja
Prediction of Cooling Load of An Energy Station based on GA-SVR
2019
IOP Conference Series: Earth and Environment
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output parameters to establish the SVR(Support Vector Regress)cooling load prediction model,the key parameters of SVR are optimized by GA(Genetic Algorithm).The results show that the maximum absolute ...
error between the predicted value and the actual value is 4.83 GJ/h, the maximum relative error is 9.2 %, the average absolute error is 1.25 GJ/h, and the average relative error is 2.4 %. ...
The genetic algorithm is used to optimize the parameters of the support vector machine. ...
doi:10.1088/1755-1315/300/4/042007
fatcat:nh5mtu63vzdgdlumjhe2ioue5a
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