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Dynamic Forecasting of Solar Energy Microgrid Systems Using Feature Engineering

IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2022年第6期58卷 7857-7869页

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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Multi-factor offshore short-term wind power prediction based on xgboost 35

Multi-factor offshore short-term wind power prediction based...

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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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Photovoltaic Power Prediction Model Using Pre-train and Fine-tune Paradigm Based on LightGBM and xgboost 18

Photovoltaic Power Prediction Model Using Pre-train and Fine...

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18th International Conference on Future Networks and Communications (FNC) / 20th International Conference on Mobile Systems and Pervasive Computing (MobiSPC) / 13th International Conference on Sustainable Energy Information Technology (SEIT)

作者： Ye, Jiandong Zhao, Binqi Deng, Hua Guangdong Power Grid Co Ltd Yangjiang Power Supply Bur Yangjiang Peoples R China Inst Trop & Marine Meteorol Guangzhou Peoples R China

Accurate photovoltaic power prediction is crucial for optimizing the performance of photovoltaic power plants and ensuring the stability of the power grid. However, existing forecasting methods are challenged when predicting the power output of multiple plants with different geographical locations and environmental conditions simultaneously, due to the complexity of the underlying factors that affect power generation. To address this issue, this paper proposes a novel LightGBM-xgboost photovoltaic power prediction model based on a pre-training and fine-tuning paradigm. The model leverages a large dataset to pre-train and learn rich feature representations within meteorological elements, which are then fine-tuned on specific downstream tasks to improve prediction accuracy and generalization ability. Experimental verification is conducted using data from three photovoltaic generators operating in Guangdong, China. Results demonstrate that the proposed model outperforms other methods when predicting the power output of multiple plants with different conditions simultaneously, highlighting the effectiveness of the proposed approach. Overall, this work contributes to advancing the state-of-the-art in photovoltaic power prediction and has practical implications for improving the control and scheduling of photovoltaic power plants, as well as ensuring the stability and security of the power grid.

关键词： Photovoltaic power prediction Pre-train and Fine-tune model LightGBM algorithm xgboost algorithm

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Landslide susceptibility mapping using xgboost machine learning method

Landslide susceptibility mapping using XGBoost machine learn...

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International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing (MIGARS)

作者： Badola, Shubham Mishra, Varun Narayan Parkash, Surya Amity Univ Amity Inst Geoinformat & Remote Sensing Sect 125 Noida India Natl Inst Disaster Management GMRD Sect 29 Delhi India

ISBN: (纸本)9798350345421

The present work demonstrates a landslide hazard zonation technique based on a machine learning algorithm of Extreme Gradient Boosting (xgboost). The xgboost algorithm uses a multi-parameter indices function as an input, namely Slope, Aspect, Curvature, topographic wetness index (TWI), Stream Power Index (SPI), Rainfall and Normalized Difference Vegetation Index (NDVI). These indices were systematically investigated using ALOS PALSAR DEM image (12.5m resolution), rainfall from WorldClim data and Landsat-5 data for NDVI mapping. Initially, the hierarchical selection of the causative factors was carried out within the xgboost algorithm, which revealed the variable importance of each causative factor to be used for the parameterization of the landslide potential index (LPI). LPI mapping indicated five different classes of landslide zonation, which were tested using the ROC (receiver operating characteristic) curve. The AUC (Area under the curve) yielded a value of 0.91, indicating a robust performance of the ROC in terms of a true positive rate. A significantly larger spatial extent of the study area was found in moderate to high zones along the road, indicating the potential impact of governing causative factors of landslide vulnerability.

关键词： Landslide Potential Index Landslide Risk xgboost algorithm receiver operating characteristic-Area under the curve

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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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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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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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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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An Advisory Student Achievement Model Based on Data Mining Techniques 5

An Advisory Student Achievement Model Based on Data Mining T...

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5th International Conference on Computing and Informatics (IEEE ICCI)

作者： Zaied, Abdel Nasser H. Hamza, Ehab Moh. Ismael, Rana Wael Misr Int Univ MIU Fac Comp Sci Cairo Egypt Helwan Univ Educ Technol Dept Cairo Egypt Helwan Univ Fac Commerce & Business Adm Cairo Egypt

ISBN: (纸本)9781665499736;9781665499729

Predicting student achievement is considered one of the most essential components of educational data mining. Academic institutions are concentrating on employing data mining techniques to improve student performance. Many prediction models have been presented to anticipate student accomplishment at an early stage in order to take preventative measures. This research looked at past models and presented a data-mining-based advising student achievement model. This study was carried out utilizing Artificial Neural Network (ANN), Decision Tree (DT), Naive Bayes classifiers, Random Forest, Support Vector Machine (SVM), and xgboost to create a prediction model, with datasets containing 16 variables and 480 instances. Model produced satisfactory results, according to the findings of the experiments. With an accuracy of 84 percent without feature selection and 85.01 percent with feature selection using correlation, the xgboost model was the most accurate of the four models.

关键词： Education Data Mining Prediction Model Student Performance xgboost algorithm

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