Power System Oscillation Mode Prediction Based on the Lasso Method

Mo, Weike; Lv, Jiaqing; Pawlak, Mirosław; Annakkage, U.D.; Chen, Haoyong

doi:10.1109/access.2020.2980983

Cited by 8 publications

(3 citation statements)

References 32 publications

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“…where n is the number of observations, p is the dimension of variables, β represents the regression coefficients, and λ is a tuning parameter that controls the compression of regression [22,23].…”

Section: Lasso Regressionmentioning

confidence: 99%

Scenario analysis of transportation carbon emissions in China based on machine learning and deep neural network models

Tang

Ri-zhao

Wang

et al. 2023

Environ. Res. Lett.

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Coping with the relation between the increase in carbon emissions and energy consumption in the transportation sector is a pressing issue today. Machine learning and deep neural networks were used in this study to explore the influential factors and trends in future transportation carbon emissions. First, the least absolute shrinkage and selection operator (LASSO) regression was adopted to screen out the key influencing factors in transportation carbon emissions. Second, the prediction performance of the long short-term memory (LSTM) network, generalized regress neural network (GRNN), and back propagation (BP) network were compared, and an improved LSTM optimized by the sparrow search algorithm (SSA) was proposed. Third, LASSO-SSA-LSTM was used to predict the transportation sector’s future carbon emissions trends under different scenarios. The results suggested that transportation carbon emissions in China presented a trend of “rapid increase - fluctuating decrease - continuous increase” from 2010 to 2019. Although the main determinant in curbing the rising rate of carbon emissions effectively is the continuous development of new clean energy technology, the variation in transportation carbon emissions in China under eight scenarios showed significant differences. Generally, systemic changes and innovations are crucial to accommodate China’s future low-carbon and sustainable transportation development.

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Section: Lasso Regressionmentioning

confidence: 99%

Scenario analysis of transportation carbon emissions in China based on machine learning and deep neural network models

Tang

Ri-zhao

Wang

et al. 2023

Environ. Res. Lett.

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show abstract

“…In addition, it studies the identification problem of broadband oscillation based on an artificial intelligence algorithm. A large amount of historical data is needed for training to obtain more accurate analysis results [19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

A New Method for the Analysis of Broadband Oscillation Mode of New Energy Stations

Guo,

Yang,

Dai

et al. 2024

Symmetry

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The accurate identification of the broadband oscillation mode is the premise of solving the resonance risk of new energy stations. Reviewing the traditional Prony algorithm, the problems of the high model order and poor noise immunity in broadband oscillation mode are identified. The accuracy and running time of the Variational mode decomposition (VMD) is a symmetric trade-off problem. An improved strategy based on VMD is proposed. Firstly, the optimal value of the number of modes and penalty factors obtained by a particle swarm optimization algorithm is input into VMD to decompose the signal into multiple modes. Then, combined with the energy threshold method, the denoising and signal reconstruction of each mode component after decomposition are carried out. Finally, the Prony algorithm is used to identify the oscillation mode of the original signal and the reconstructed signal, respectively. The Signal-to-noise ratio (SNR) and model order are compared and analyzed. Through the analysis of the example and simulation data, it is shown that the proposed method effectively solves the problem of poor engineering adaptability of the traditional Prony algorithm. It also can accurately obtain the time-domain characteristics of broadband oscillation, which has a promising future in the engineering application.

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“…Since then, the Lasso has become a popular approach in modern-day high-dimensional statistics [4]. It has been applied in biometrics [22], power systems [20,23], energy [21], etc. So far as we know, it has not been applied in pollutant modeling areas yet.…”

Section: Introductionmentioning

confidence: 99%

Prediction of daily maximum ozone levels using Lasso sparse modeling method

Lv¹,

2020

Preprint

Self Cite

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This paper applies modern statistical methods in prediction of the next-day maximum ozone concentration, as well as the maximum 8-hour-mean ozone concentration of the next day. The model uses a large number of candidate features, including the present day's hourly concentration level of various pollutants, as well as the meteorological variables of the present day's observation and the future day's forecast values. In order to solve such an ultra-high dimensional problem, the least absolute shrinkage and selection operator (Lasso) was applied. The 1 nature of this methodology enables the automatic feature dimension reduction, and a resultant sparse model. The model trained by 3-years data demonstrates relatively good prediction accuracy, with RMSE= 5.63 ppb, MAE= 4.42 ppb for predicting the next-day's maximum 3 concentration, and RMSE= 5.68 ppb, MAE= 4.52 ppb for predicting the next-day's maximum 8-hour-mean 3 concentration. Our modeling approach is also compared with several other methods recently applied in the field, and demonstrates superiority in the prediction accuracy.

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Power System Oscillation Mode Prediction Based on the Lasso Method

Cited by 8 publications

References 32 publications

Scenario analysis of transportation carbon emissions in China based on machine learning and deep neural network models

Scenario analysis of transportation carbon emissions in China based on machine learning and deep neural network models

A New Method for the Analysis of Broadband Oscillation Mode of New Energy Stations

Prediction of daily maximum ozone levels using Lasso sparse modeling method

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