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Applied Energy Symposium and Forum - Low-Carbon Cities and Urban Energy Systems (CUE)

作者： Ma, Zhitong Ye, Cantao Li, Huashan Ma, Weibin Chinese Acad Sci Guangzhou Inst Energy Convers Guangzhou 510640 Guangdong Peoples R China CAS Key Lab Renewable Energy Guangzhou 510640 Guangdong Peoples R China Guangdong Prov Key Lab New & Renewable Energy Res Guangzhou 510640 Guangdong Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Shenzhen Acad Metrol & Qual Inspect Shenzhen 518055 Peoples R China

It is increasingly significant to predict building energy consumption (BEC) in energy-saving decision making. This paper presents the method of support vector machines (SVM) to forecast building energy consumption in China. To improve the reliability of SVM in building energy consumption prediction, multiple parameters including weather data such as yearly mean outdoor dry-bulb temperature, relative humidity and global solar radiation and economic factors such as the ratio of urbanization, gross domestic product, household consumption level and total area of structure are taken as inputs. The performance of SVM with respect to two parameters, c and g, is explored using the k-fold cross validation with grid searching method based on radial basis function kernel. Finally, the performance of the proposed model is checked by comparing the prediction model with statistic data taken from Chinese National Bureau of Statistics in 30 provinces (including autonomous regions and municipalities) of China using statistical error tests such as the mean square error (MSE) and the squared correlation coefficient (r(2)). With the analysis based on statistical error tests, the results show that the SVM method can estimate the building energy consumption with good accuracy with MSE less than 1E-3 and r(2) more than 0.991. Copyright (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Support vector machine Building energy consumption Weather data Economic factor Cross validation grid search algorithm

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Feature Selection and Hyperparameter Optimization of SVM for Human Activity Recognition 3

Feature Selection and Hyperparameter Optimization of SVM for...

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3rd International Conference on Soft Computing and Machine Intelligence (ISCMI)

作者： Sunkad, Zubin A. Soujanya PESIT Dept Elect & Commun Bengaluru India

ISBN: (纸本)9781509036967

Activity recognition has received a lot of attention from research scholars in the past few years. There has been a huge demand for activity recognition because of its ability to ease human-machine interaction, help in care for the elderly, and monitor the habitat requirements of the wildlife. In this paper, a Support Vector Machine (SVM) classifier to recognize the human activities has been built. Data was collected from the database provided by the University of Southern California (USC) for human activity recognition. Six features were computed to obtain the feature set. Different feature subsets were then evaluated based on the precision and recall scores. Using grid search algorithm, the best subset of hyperparameters (SVM kernel, regularization parameter(C) and gamma) for the SVM classifier which gives the highest precision and recall score was selected from the parameter space. The best set of features and the SVM hyperparameters for obtaining best results in activity recognition are proposed in this work.

关键词： Activity Recognition Feature Selection SVM Hyperparameters grid search algorithm precision recall Linear kernel Radial basis function(RBF) Regularization parameter (C) gamma Confusion matrix

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Data Classification Based on the Hybrid Versions of the Particle Swarm Optimization algorithm 7

Data Classification Based on the Hybrid Versions of the Part...

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7th Mediterranean Conference on Embedded Computing (MECO)

作者： Demidova, Liliya Klyueva, Irina Ryazan State Radio Engn Univ Ryazan Russia

ISBN: (纸本)9781538656839

The paper presents two hybrid versions of the basic PSO algorithm, involving the use of the classical grid search (GS) algorithm and Design of Experiment (DOE) algorithm correspondingly. These hybrid versions have been applied to the problem of search of the parameters values of the SVM classifier. The results of experimental studies confirm the application efficiency of the hybrid versions of the basic PSO algorithm with the aim of reducing of the time expenditures for searching the optimum parameters of the SVM classifier while maintaining of high quality of its classification decisions.

关键词： classification particle swarm optimization algorithm grid search algorithm SVM classifier radial basis kernel function

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Operation Schemes of Medium Voltage Distribution Networks with High Penetration of Distributed Solar Photovoltaics

Operation Schemes of Medium Voltage Distribution Networks wi...

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IEEE Asia Meeting on Environment and Electrical Engineering (IEEE-AM)

作者： Phuc-Huy Nguyen Hoang-Giang Vu Elect Power Univ Elect Engn Fac Hanoi Vietnam

ISBN: (纸本)9798350381061

The rapid development of small and mediumsized solar photovoltaics connected to the power distribution grids has led to many impacts on the management and operation of the grids. With a high penetration level, the power generated from these sources flows back to intermediate substations (at the level of 110 kV in Vietnam), leading to the risk of power transformer overload and increased losses on the grids. The paper focuses on calculation and analysis in different scenarios of these distributed solar photovoltaics in order to propose applicable operation schemes. The proposed grid restructuring solution using grid search algorithm shows the prospect in reducing losses and improving voltage quality to the grid. Results of the paper obtained from a case study of specific medium-voltage grid of Vietnam will be a practical reference for distribution grid management and operation utilities.

关键词： grid search algorithm power distribution network solar photovoltaic (PV) power loss reconfiguration.

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Intellectual approaches to improvement of the classification decisions quality on the base of the SVM classifier 12th

Intellectual approaches to improvement of the classification...

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12th International Symposium on Intelligent Systems (INTELS)

作者： Demidova, L. Klyueva, I. Sokolova, Y. Stepanov, N. Tyart, N. Ryazan State Radio Engn Univ 59-1Gagarina Str Ryazan 390005 Russia Moscow Technol Inst 38ALeninskiy pr Moscow 119334 Russia

In this paper the hybrid and modified versions of the PSO algorithm applied to improvement of the search characteristics of the classical PSO algorithm in the development problem of the SVM classifier have been offered and investigated. A herewith two hybrid versions of the PSO algorithm assume the use of the classical "grid search" (GS) algorithm and the "Design of Experiment" (DOE) algorithm accordingly, and the modified version of the PSO algorithm realizes the simultaneous search of the kernel function type, the parameters values of the kernel function, and also the regularization parameter value. Besides, the questions of applicability of the k nearest neighbors (kNN) algorithm in the development problem of the SVM classifier have been considered. (C) 2017 The Authors. Published by Elsevier R.V.

关键词： support vector machine particle swarm optimization algorithm grid search algorithm hybrid algorithm classification optimization parameters of the kernel function regularization parameter separating hyperplane support vectors

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Student Pass Rates Prediction Using Optimized Support Vector Machine and Decision Tree 8

Student Pass Rates Prediction Using Optimized Support Vector...

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8th IEEE Annual Computing and Communication Workshop and Conference (CCWC)

作者： Ma, Xiaofeng Zhou, Zhurong Southwest Univ Coll Comp & Informat Sci Chongqing Peoples R China

ISBN: (纸本)9781538646496

Since student performance and pass rates in school reflect teaching level of the school and even all education system, it is critical to improve student pass rates and reduce dropout rates. Decision Tree (DT) algorithm and Support Vector Machine (SVM) algorithm in data mining, have been used by researchers to find important student features and predict the student pass rates, however they did not consider the coefficient of initialization, and whether there is a dependency between student features. Therefore, in this study, we propose a new concept: features dependencies, and use the grid search algorithm to optimize DT and SVM, in order to improve the accuracy of the algorithm. Furthermore, we added 10-fold cross-validation to DT and SVM algorithm. The results show the experiment can achieve better results in this work. The purpose of this study is providing assistance to students who have greater difficulties in their studies, and students who are at risk of graduating through data mining techniques.

关键词： Features Dependencies Initialization Coefficient Rules grid search algorithm Decision Tree Support Vector Machine

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An Intelligent Learning System Based on Random search algorithm and Optimized Random Forest Model for Improved Heart Disease Detection

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IEEE ACCESS 2019年 7卷 180235-180243页

作者： Javeed, Ashir Zhou, Shijie Liao Yongjian Qasim, Iqbal Noor, Adeeb Nour, Redhwan Univ Elect Sci & Technol China Sch Informat & Software Engn Chengdu 611731 Peoples R China Univ Sci & Technol Bannu Dept Comp Sci Bannu 28100 Pakistan King Abdulaziz Univ Fac Comp & Informat Technol Dept Informat Technol Jeddah 80221 Saudi Arabia Taibah Univ Dept Comp Sci Medina 42353 Saudi Arabia

Heart failure is considered one of the leading cause of death around the world. The diagnosis of heart failure is a challenging task especially in under-developed and developing countries where there is a paucity of human experts and equipments. Hence, different researchers have developed different intelligent systems for automated detection of heart failure. However, most of these methods are facing the problem of overfitting i.e. the recently proposed methods improved heart failure detection accuracy on testing data while compromising heart failure detection accuracy on training data. Consequently, the constructed models overfit to the testing data. In order, to come up with an intelligent system that would show good performance on both training and testing data, in this paper we develop a novel diagnostic system. The proposed diagnostic system uses random search algorithm (RSA) for features selection and random forest model for heart failure prediction. The proposed diagnostic system is optimized using grid search algorithm. Two types of experiments are performed to evaluate the precision of the proposed method. In the first experiment, only random forest model is developed while in the second experiment the proposed RSA based random forest model is developed. Experiments are performed using an online heart failure database namely Cleveland dataset. The proposed method is efficient and less complex than conventional random forest model as it produces 3.3% higher accuracy than conventional random forest model while using only 7 features. Moreover, the proposed method shows better performance than five other state of the art machine learning models. In addition, the proposed method achieved classification accuracy of 93.33% while improving the training accuracy as well. Finally, the proposed method shows better performance than eleven recently proposed methods for heart failure detection.

关键词： Heart failure hyperparameters optimization feature selection random search algorithm grid search algorithm

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Prediction of Plasticizer Property Based on an Improved Genetic algorithm

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POLYMERS 2022年第20期14卷 4284页

作者： Zhang, Yuyin Deng, Ningjie Zhang, Shiding Liu, Pingping Chen, Changjing Cui, Ziheng Chen, Biqiang Tan, Tianwei Beijing Univ Chem Technol Natl Energy R&D Ctr Biorefinery Beijing 100029 Peoples R China

Different plasticizers have obvious differences in plasticizing properties. As one of the important indicators for evaluating plasticization performance, the substitution factor (SF) has great significance for product cost accounting. In this research, a genetic algorithm with "variable mutation probability" was developed to screen the key molecular descriptors of plasticizers that are highly correlated with the SF, and a SF prediction model was established based on these filtered molecular descriptors. The results show that the improved genetic algorithm greatly improved the prediction accuracy in different regression models. The coefficient of determination (R-2) for the test set and the cross-validation both reached 0.92, which is at least 0.15 higher than the R-2 of the unimproved genetic algorithm. From the results of the selected descriptors, most of the descriptors focused on describing the branching of the molecule, which is consistent with the view that the branching chain plays an important role in the plasticization process. As the first study to establish the relationship between plasticizer SF and plasticizer molecular structure, this work provides a basis for subsequent plasticizer performance and evaluation system modeling.

关键词： plasticizer substitution factor machine learning genetic algorithm grid search algorithm

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Evaluation of failure criteria under dynamic loading of the wellbore with strain rates between 10^-6 and 10⁰ /s

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2021年 201卷 108448-108448页

作者： Meng, Meng Baldino, Silvio Miska, Stefan Los Alamos Natl Lab EES 14 Los Alamos NM USA Shell Houston TX USA Univ Tulsa Dept Petr Engn Tulsa OK 74104 USA

Dynamic loading is very common during drilling, mining, hydraulic fracturing, and tunnel excavation. This could compromise the long-term stability and integrity of the underground constructions. Under dynamic loading, rocks usually behave differently compared to static loading. Whether the static failure criteria can be used to evaluate structure failure under dynamic loading is a question that has not been answered yet. In this paper, the triaxial compression strength data under different strain rates (between 10(-6) and 10(0)/s) for four types of rock (Bedford limestone, Bukit Timah granite, Laurentian granite, and Dry silica sand) have been used to evaluate six different failure criteria. Two methods have been implemented to obtain rock cohesion and internal friction angle, which are the grid search algorithm and the Mohr failure line method. The results show that the grid search algorithm guarantees the best performance of the failure criteria, providing the best-fit rock parameters. The more practical method of the Mohr failure line can also generate reasonable predictions if a safety factor of 1.1 is considered. Both methods show that Modified Lade is the failure criterion that provides the best performance in predicting rock failure under dynamic loading. In the petroleum industry, most of the activities causing dynamic loading of the wellbore are characterized by a magnitude of strain rate lower than 10(0)/s (even lower than 10(-3)/s). Consequently, this work shows that it is reasonable to assume that the commonly used static failure criteria, especially Modified Lade, could be selected to analyze wellbore stability under dynamic loading conditions effectively.

关键词： Failure criteria Strain rates grid search algorithm Mohr failure line Triaxial compression tests

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Newbuilding ship price forecasting by parsimonious intelligent model search engine

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EXPERT SYSTEMS WITH APPLICATIONS 2022年第0期201卷 1页

作者： Gao, Ruobin Liu, Jiahui Zhou, Qin Duru, Okan Yuen, Kum Fai Nanyang Technol Univ Sch Civil & Environm Engn Singapore Singapore Ocean Dynamex Ottawa ON Canada

Asset prices play a significant role in the financial survival and profitability of ship-owning firms. In a highly volatile shipping market, prices of newbuilding ships must be predicted to detect security shortfalls as well as opportunities for temporal arbitration (gaining on high-low pricing). Accordingly, this paper proposes an improved version of the intelligent model search engine (IMSE) by asynchronous time lag selection. The parsimonious IMSE algorithm comprises the essential components such as input and training data size selection by a grid search procedure. In the initial IMSE algorithm, time-lag (memory size) selection is designed such that a serial cluster of memory groups is assigned synchronously for all inputs. By relaxing of lag structures selection, the proposed algorithm estimates unique lead-lag relations for the input of the intended problem set. An extensive benchmark study with several baseline models and the persistence forecast (Naive I) is performed to observe the out-of-sample accuracy of the proposed approach. The empirical results indicate that second-hand ship prices, scrap values, and orderbook (no. of orders) have predictive features and are selected by the search engine for two ship sizes. Different lag structures are estimated for each input with asynchronous time-lag selection improvement.

关键词： Forecasting Shipping market grid search algorithm Machine learning

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