Degradation Trend Prediction of Pumped Storage Unit Based on MIC-LGBM and VMD-GRU Combined Model

Chen, Peng; Deng, Yumin; Zhang, Xuegui; Ma, Li; Yan, Yaoliang; Wu, Youfeng; Li, Chaoshun

doi:10.3390/en15020605

Cited by 9 publications

(6 citation statements)

References 48 publications

(51 reference statements)

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“…Among the most commonly used time series decomposition methods are WT, empirical mode decomposition (EMD) [19], ensemble empirical mode decomposition (EEMD) [20], and VMD [21]. All these techniques have been successfully employed for diverse time series forecasting problems from financial market analysis [22][23][24], to earthquake and weather predictions [25,26], to machine health monitoring [27,28].…”

Section: Signal Decomposition Techniquesmentioning

confidence: 99%

Predictive Analytics of Air Temperature in Alaskan Permafrost Terrain Leveraging Two-Level Signal Decomposition and Deep Learning

Ahajjam,

Putkonen,

Chukwuemeka

et al. 2024

Forecasting

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Local weather forecasts in the Arctic outside of settlements are challenging due to the dearth of ground-level observation stations and high computational costs. During winter, these forecasts are critical to help prepare for potentially hazardous weather conditions, while in spring, these forecasts may be used to determine flood risk during annual snow melt. To this end, a hybrid VMD-WT-InceptionTime model is proposed for multi-horizon multivariate forecasting of remote-region temperatures in Alaska over short-term horizons (the next seven days). First, the Spearman correlation coefficient is employed to analyze the relationship between each input variable and the forecast target temperature. The most output-correlated input sequences are decomposed using variational mode decomposition (VMD) and, ultimately, wavelet transform (WT) to extract time-frequency patterns intrinsic in the raw inputs. The resulting sequences are fed into a deep InceptionTime model for short-term forecasting. This hybrid technique has been developed and evaluated using 35+ years of data from three locations in Alaska. Different experiments and performance benchmarks are conducted using deep learning models (e.g., Time Series Transformers, LSTM, MiniRocket), and statistical and conventional machine learning baselines (e.g., GBDT, SVR, ARIMA). All forecasting performances are assessed using four metrics: the root mean squared error, the mean absolute percentage error, the coefficient of determination, and the mean directional accuracy. Superior forecasting performance is achieved consistently using the proposed hybrid technique.

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Section: Signal Decomposition Techniquesmentioning

confidence: 99%

Predictive Analytics of Air Temperature in Alaskan Permafrost Terrain Leveraging Two-Level Signal Decomposition and Deep Learning

Ahajjam,

Putkonen,

Chukwuemeka

et al. 2024

Forecasting

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“…LightGBM is a decision tree-based, fast gradient boosting approach, it offers improved computational efficiency and accuracy [26] [27]. Using exclusive functional grouping and histogram-based techniques, it eliminates occurrences with minor gradients and concentrates on those with big gradients to calculate information gain and decrease feature dimension [28].…”

Section: Boosting Ensemble Learningmentioning

confidence: 99%

A Hybrid Ensemble Learning Approach Utilizing Light Gradient Boosting Machine and Category Boosting Model for Lifestyle-Based Prediction of Type-II Diabetes Mellitus

Nagassou,

Mwangi,

Nyarige

2023

JDAIP

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Addressing classification and prediction challenges, tree ensemble models have gained significant importance. Boosting ensemble techniques are commonly employed for forecasting Type-II diabetes mellitus. Light Gradient Boosting Machine (LightGBM) is a widely used algorithm known for its leaf growth strategy, loss reduction, and enhanced training precision. However, LightGBM is prone to overfitting. In contrast, CatBoost utilizes balanced base predictors known as decision tables, which mitigate overfitting risks and significantly improve testing time efficiency. CatBoost's algorithm structure counteracts gradient boosting biases and incorporates an overfitting detector to stop training early. This study focuses on developing a hybrid model that combines LightGBM and CatBoost to minimize overfitting and improve accuracy by reducing variance. For the purpose of finding the best hyperparameters to use with the underlying learners, the Bayesian hyperparameter optimization method is used. By fine-tuning the regularization parameter values, the hybrid model effectively reduces variance (overfitting). Comparative evaluation against LightGBM, CatBoost, XGBoost, Decision Tree, Random Forest, AdaBoost, and GBM algorithms demonstrates that the hybrid model has the best F1-score (99.37%), recall (99.25%), and accuracy (99.37%). Consequently, the proposed framework holds promise for early diabetes prediction in the healthcare industry and exhibits potential applicability to other datasets sharing similarities with diabetes.

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“…Variational mode decomposition (VMD) is a new adaptive mode variational extraction method for nonstationary signals, which can realize effective separation of intrinsic mode component (VIMF) and frequency domain division of signals, thus effectively reducing the non-stationarity of complex nonlinear time series [4] . Therefore, the VMD method is adopted in this paper to preprocess the historical data of the power communication service traffic sequence of the cloud platform, and the traffic sequence is extracted into multiple intrinsic mode components and residual components, to reduce the difficulty of subsequent LSTM neural network training.…”

Section: Vmd Preprocessingmentioning

confidence: 99%

Power communication digital flow prediction method based on VMD-LSTM-SVM model

Wang,

Zhang,

Zhang

et al. 2023

J. Phys.: Conf. Ser.

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Under the current trend of abundant information on power business, large data concentration, and large flow explosion, aiming at the randomness, volatility, and uncertainty of massive flow of electric power communication network, a digital power flow prediction method based on VMD-LSTM-SVM model is proposed. The interaction between the values of each traffic index before and after time is considered. LSTM is used to process traffic data and make an accurate prediction of future traffic. The power communication network can make dispatch responses to possible communication congestion by using link resources according to traffic prediction results and ensuring the transmission quality of power service data.

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Degradation Trend Prediction of Pumped Storage Unit Based on MIC-LGBM and VMD-GRU Combined Model

Cited by 9 publications

References 48 publications

Predictive Analytics of Air Temperature in Alaskan Permafrost Terrain Leveraging Two-Level Signal Decomposition and Deep Learning

Predictive Analytics of Air Temperature in Alaskan Permafrost Terrain Leveraging Two-Level Signal Decomposition and Deep Learning

A Hybrid Ensemble Learning Approach Utilizing Light Gradient Boosting Machine and Category Boosting Model for Lifestyle-Based Prediction of Type-II Diabetes Mellitus

Power communication digital flow prediction method based on VMD-LSTM-SVM model

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