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35th Chinese Control and Decision Conference (CCDC)

作者： Zhang, Yuhan Wang, Bin Xu, Wanwan Wuhan Univ Sci & Technol Wuhan Peoples R China Wuhan Univ Sci & Technol Sch Informat Sci & Engn Wuhan Peoples R China

ISBN: (纸本)9798350334722

Fast and accurate wind power prediction is important for grid-connected operation and grid dispatch of offshore wind power. A gated recurrent neural network (GRU) multi-factor prediction model based on extreme gradient boosting (xgboost) algorithm and particle swarm optimization is proposed to address the problems of random fluctuation of offshore wind power, susceptibility to climate factors, and poor prediction accuracy due to large data volume. The relevance and influence of historical offshore wind power, wind speed, wind direction and temperature are analyzed, ranked in order of importance and downscaled, and then predicted using an optimized gated recurrent neural network. The results are validated with real data from an offshore wind farm and show a decrease in MAE, RMSE and SMAPE. The results show that the MAE, RMSE and SMAPE are all reduced, and that the MAE is still highly accurate when the offshore wind farm is in a different season or in a different location, thus demonstrating the adaptability of the model.

关键词： offshore wind power power prediction gated cycle unit xgboost algorithm neural networks

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Research on loyalty prediction of e-commerce customer based on data mining

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APPLIED MATHEMATICS AND NONLINEAR SCIENCES 2022年第2期8卷 721-732页

作者： Qin, Xujie Anhui Business & Technol Coll Hefei 231131 Anhui Peoples R China

Analysing the data generated by the daily operations of enterprises through data mining technology can effectively predict customer loyalty and help enterprise leaders make correct decisions. Therefore, this paper classifies and analyses churn and loyalty of e-commerce customers, and by combining the application foundation of data mining technology in e-commerce customer loyalty prediction, a prediction model of e-commerce customer loyalty based on data mining is constructed. In this model, the local abnormal factor algorithm is used to eliminate the data for cleaning, the xgboost algorithm is improved by adding penalty coefficient, and the prediction effect of the model is evaluated and compared according to the values of Accuracy, Precision, Recall and F. The results show that the model has high accuracy in predicting customer loyalty, which can accurately extract attributes of users and characteristic information of commodities.

关键词： Data mining e-commerce customer loyalty xgboost algorithm

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Load Demand Forecasting in Smart Microgrid Using NWP via Feature Engineering

Load Demand Forecasting in Smart Microgrid Using NWP via Fea...

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2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023

作者： Mohamed, Muamar Mahmood, Farhad E. Abd, Mehmmood A. Rezkallah, Miloud Hamadi, Abdelhamid Chandra, Ambrish Ecole de Technologie Superieure University of Quebec Department of Electrical Engineering MontrealQC Canada University of Mosul Department of Electrical Engineering Mosul Iraq Electrical and Computer Engineering Ibm United States

ISBN: (纸本)9781665471640

This study developed a machine learning model to forecast electrical demand for smart micro-grids with the existing of numerical weather forecasting (NWP). The model uses three techniques, linear regression (LR), Long Short-Term Memory (LSTM) and eXtreme Gradient Boosting (xgboost), to harness insights from historical data and predict future load patterns. Notably, the xgboost algorithm is highlighted as the most effective due to underscoring the importance of feature selection and model optimization. The utilization of these techniques results in accurate electrical demand predictions, which are crucial for enhancing micro-grid efficiency, practically in response to weather-induced load fluctuations. This paper suggests employing advanced feature selection methods based on high feature importance, ultimately refining the model's predictive capabilities for smart micro-grid applications. © 2023 IEEE.

关键词： forecasting techniques Load demand machine learning micro-grid NWP xgboost algorithm

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The Implementation of Integration of ML algorithm for the Design of Stability System for Grid System

The Implementation of Integration of ML Algorithm for the De...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Chandani, Priyanka Vijay, Sandip Soumya, K. Noida Institute of Engineering and Technology Department of Data Science Uttar Pradesh Greater Noida India Tula's Institute Department of Computer Science and Engineering Dehradun India Department of Computer Science Bangalore India

ISBN: (纸本)9798350357769

A modern decentralized electric grid is a groundbreaking system that integrates demand response effortlessly and doesn't need major infrastructure changes. Within the decentralized domain, users independently control their power consumption according to the frequency of the grid. This is made possible by the use of reasonably priced devices like smart meters, which allow grid frequency to be measured from almost anywhere. Different data-level resampling strategies have been used to address the problem of data imbalance, while data normalization approaches have been used to reduce biased behavior among characteristics. The findings clearly show that in terms of the performance of classifiers, a balanced dataset performs better than an unbalanced one. Specifically, for unbalanced datasets, oversampling approaches are more effective than under sampling ones. With a precision level of 94.7 percent, the xgboost algorithm is the best performer within the range of deep learning algorithms that are taken into consideration. Remarkably, xgboost's accuracy forecast rises to 96.8% when paired with random oversampling. This improved model manages the volatility of renewable energy supplies and maximizes their use by accurately forecasting frequency variations in decentralized power networks. This model's predictive capacities have great potential to support the stability of distributed electricity grids, which will improve the distribution and administration of energy on a larger scale. © 2023 IEEE.

关键词： Data Normalization Data Resampling Decentralized Power Grid Demand Response and Classifiers Performance Energy Policy Grid Stability Machine Learning Renewable Energy Integration Renewable Sources Smart Meters Stability Prediction xgboost algorithm

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xgboost based Short-term Electrical Load Forecasting Considering Trends & Periodicity in Historical Data

XgBoost based Short-term Electrical Load Forecasting Conside...

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2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023

作者： Shabbir, Noman Ahmadiahangar, Roya Rosin, Argo Jawad, Muhammad Kilter, Jako Martins, Joao Tallinn University of Technology FinEST Center for Smart Cities Tallinn Estonia Nova University Department of Electrical & Computer Engineering Lisbon Portugal Tallinn University of Technology Department of Electrical Power Engineering Tallinn Estonia Comsats University Department of Electrical & Computer Engineering Lahore Pakistan

ISBN: (纸本)9781665471640

The effective planning and management of residential electricity demand requires precise forecasting of the short-term electrical load. A novel approach is proposed for short-term electrical load forecast employing the xgboost algorithm in this research work which is based on feature selection by considering the trends and patterns in the historical dataset. This xgboost algorithm combines several weak learners to create a strong predictive model. The trained model is capable of capturing the nonlinear relationships and complex patterns present in real-world residential load data. The simulation results reveal that the xgboost algorithm outperforms traditional forecasting techniques in terms of accuracy when trained on feature selection based on data analytics. The accuracy of the forecast is assessed using standard metrics such as root mean square error (RMSE), mean absolute percentage error (MAPE), mean square error (MSE), and mean absolute error (MAE). The proposed approach achieves significant improvements for a 24-hour-ahead electrical load forecast for domestic users. © 2023 IEEE.

关键词： CAT boost Load forecast Machine learning Residential load xgboost algorithm

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An adaptive prediction model for sparse data forecasting

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INTERNATIONAL JOURNAL OF AUTONOMOUS AND ADAPTIVE COMMUNICATIONS SYSTEMS 2022年第4期15卷 331-344页

作者： Yao, Xuan Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing 210044 Jiangsu Peoples R China

Sparse data generated by the limitations of data acquisition are ubiquitous for prediction. However, the general prediction model is challenging to deal with those sparse data. Therefore, this paper aims to propose an adaptive sparse data prediction model. Firstly, we introduced the aXreme Gradient Boosting (xgboost) algorithm to build an adaptive prediction model to correct sparse data constantly. Secondly, the sparsity perception of the xgboost algorithm is used for parallel tree learning. Finally, we applied the model to the PM2.5 concentration forecasting of Nanjing, China. We trained the model and adjusted the parameters to get better prediction results, and compared the prediction results with actual data to prove the feasibility of the model.

关键词： adaptive prediction model sparse data xgboost algorithm PM2.5 concentration forecasting

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Dynamic Forecasting of Solar Energy Microgrid Systems Using Feature Engineering

Dynamic Forecasting of Solar Energy Microgrid Systems Using ...

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IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES)

作者： Mohamed, Muamar Mahmood, Farhad E. E. Abd, Mehmmood A. A. Chandra, Ambrish Singh, Bhim Univ Quebec Ecole Technol Super Dept Elect Engn Montreal PQ H3C 1K3 Canada Univ Mosul Dept Elect Engn Mosul 00964 Iraq IBM Corp Austin TX 78758 USA Indian Inst Technol Delhi New Delhi 110016 India

The intermittent and stochastic nature of solar energy generation systems, climate change, and the inefficiency of modern power systems due to zero inertia have created many challenges for on-grid operators. Solar forecasting systems based on machine learning algorithms are an emerging and effective solution that uses Big Data related to weather phenomena. However, the predictive ability of these algorithms is hampered by the sporadic nature of solar energy generation. In this article, a robust hybrid machine learning system that utilizes multiple linear regression (MLR) and a Pearson correlation coefficient (PCC) was tested on solar power plant sites of varying capacities in Germany (100-8500 kW). The volume of Big Data features can be reduced by focusing on the features that significantly improve the reliability of the mid-term forecasting system. In this way, drastic fluctuations in the prediction of photovoltaic (PV) power generation can be avoided. The results of our approach are evaluated regarding real-world data using the extreme gradient boosting (xgboost) with feature engineering, and principal component analysis (PCA), in order to forecast PV energy, rank, and track the importance of feature engineering for different PV capacities. Furthermore, we found that the need for selectivity and reduction of performance error was supported by ridge regression. In addition, the proposed novel xgboost forecast system decreased the root-mean-square error (RMSE) and mean absolute error (MAE) by 30% and 18%, respectively, compared to the Autoencoder and long short-term memory (LSTM) for same datasets. Furthermore, the CoD determination coefficient (R-2) increased by 85% compared to the statistical model's autoregressive integrated moving average (ARIMA).

关键词： Artificial intelligence data mining algorithms forecasting techniques machine learning power system statistical analysis solar microgrid xgboost algorithm

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xgboost based solutions for detecting fraudulent credit card transactions

XGBoost based solutions for detecting fraudulent credit card...

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International Conference on Advanced Creative Networks and Intelligent Systems (ICACNIS) - Blockchain Technology, Intelligent Systems, and the Applications for Human Life

作者： El Kafhali, Said Tayebi, Mohammed Hassan First Univ Settat Fac Sci & Tech IR2M Lab Settat Morocco

ISBN: (纸本)9798350334449

Due to the emigration of businesses to the internet, credit cards have become a widely used tool for customers to pay for purchases both online and offline. However, fraudsters try to attack these payment tools using various techniques, and credit card fraud transactions have become dangerous. Credit card payment security is becoming an important research topic, and different ways are proposed to overcome this problem. In this article, we will propose a solution that combines the strengths of three methods to secure credit card fraud transactions. The Differential Evolution (DE) algorithm is used to select the best hyperparameters of the xgboost algorithm (eXtreme Gradient Boosting). Synthetic Minority Oversampling (SMOTE) combined with Edited Nearest Neighbour (ENN) to handle the imbalanced data issues. Finally, we will classify fraud transactions using the optimized xgboost algorithm. The proposed solution is evaluated using the European data set in terms of Accuracy, Recall, F1-score, and AUC. As a result, the proposed solution generally achieved the best score in terms of all the measurements used in this study. In terms of accuracy, we achieved 99.94%, similarly, we reached the best score in terms of the Precision score, which is 80.68%, in addition, we got 86.02% of recall score, 83.27 of F-measure, and 99.21 of AUC score. Thus, we emphasize the superiority of our proposed method over the competitive machine learning model utilized in this work.

关键词： Fraud Transactions Detection Hyperparameters Optimization Differential Evolution algorithm xgboost algorithm Synthetic Minority Oversampling Edited Nearest Neighbor

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Research on Guangxi Yulin's Poverty Return Identification Based on Integrated Learning

Research on Guangxi Yulin's Poverty Return Identification Ba...

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IEEE International Conference on Electrical Engineering, Big Data and algorithms (EEBDA)

作者： Long Jing Pan Xiuzhi Zhao Qianrong Li Junjiang Nanning Univ Coll Accounting & Auditing Nanning Guangxi Peoples R China Guangxi Univ Foreign Languages Sch Accounting Nanning Guangxi Peoples R China

ISBN: (纸本)9781665416061

2020 is the end of the poverty alleviation exit effectiveness inspection. To consolidate the achievements of poverty alleviation, promote the Rural Revitalization Strategy, and prevent poverty return will be its key means and important guarantee. At present, although many experts and scholars have studied the related fields of dynamic monitoring of poverty return, most of them focus on macro policy content, and the research on poverty return identification for the poor is still relatively lacking. At the same time, the in-depth mining and application of the accumulated large-scale targeted poverty alleviation data is insufficient. This paper finds relevant literature and combines China's achievements in precise poverty alleviation work and the construction of precise poverty alleviation systems, using Yulin's poor household record-keeping data as the research object. The three integrated learning algorithms, xgboost, LightGBM and CatBoost, were used to identify poor households in three categories, out of poverty, poor and returning to poverty. The results show that the xgboost algorithm model achieves optimal results, with an overall accuracy of 95.55% for three classification recognition, and can better identify the poor categories of the poor population. At the same time, the model's feature importance ranking can reveal the importance of povertycausing characteristics of poor households in Fuping County. By analyzing the current work flow of helpers, it is clear that good dynamic monitoring of poverty return should achieve the needs of dynamic updating of data, dynamic feedback of results and dynamic adjustment of countermeasures.

关键词： Integrated learning Rural revitalization Poverty return identification xgboost algorithm Guangxi

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TBM rock mass classification using xgboost and Interpretable Machine learning

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ADVANCED ENGINEERING INFORMATICS 2025年 66卷

作者： Nie, Yaoqi Zhang, Qian Hou, Lili Du, Lijie Zhao, Xiaolong Xue, Yujie Lin, Zhicheng Cao, Xiuxiu Wang, Zixin Shijiazhuang Tiedao Univ Sch Safety Engn & Emergency Management Shijiazhuang 050043 Peoples R China Shijiazhuang Tiedao Univ Key Lab Struct Hlth Monitoring & Control Shijiazhuang 050043 Peoples R China Shijiazhuang Tiedao Univ Sch Civil Engn Shijiazhuang 050043 Hebei Peoples R China Shijiazhuang Tiedao Univ Sch Informat Sci & Technol Shijiazhuang 050043 Peoples R China Beijing Jiaotong Univ Sch Civil Engn 3 Shangyuancun Beijing 100044 Peoples R China Nagasaki Univ Sch Engn Nagasaki 8528521 Japan

Accurate classification of surrounding rock grades during Tunnel Boring Machine (TBM) operations is essential for optimizing excavation parameters and ensuring construction safety. However, traditional classification methods face challenges, particularly in adapting to complex geological conditions and achieving high efficiency in model optimization. To address these issues, this study proposes an identification methodology based on the COA-xgboost framework. Initially, raw data is preprocessed to eliminate anomalous instances, such as those arising from equipment malfunctions or maintenance activities, using a state discrimination function. To better capture the interaction dynamics between the TBM and the surrounding rock, a novel feature parameter, specific energy, is introduced. The Relief algorithm is employed to evaluate feature importance and assign weights to TBM operational parameters, amplifying the role of critical features while minimizing interference from redundant ones. Subsequently, the Coati Optimization algorithm (COA) is integrated with the xgboost classifier, enabling global parameter optimization and enhancing the model's performance. The proposed method was validated using data from the water conveyance tunnel section of the Yinchuojiliao Project. Results demonstrate that the Relief algorithm effectively enhances the model's ability to identify critical parameters through feature weighting. The COA-optimized xgboost model achieves a significant improvement in recognition accuracy compared to traditional approaches, with accuracy gains of approximately 8%, particularly excelling under complex geological conditions such as Class IV and Class V surrounding rocks. The final model achieves an accuracy of 91.5%, with recall and F1 scores reaching 91.5% and 91.4%, respectively. Additionally, five-fold cross-validation highlights the model's robustness and generalization capabilities. Furthermore, a SHAP-based interpretability analysis clarifies the re

关键词： TBM Rock Grade Identification xgboost algorithm Coati optimization algorithm SHAP

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