Optimal allocation of customer energy storage based on power big data and improved LSTM load forecasting

Wang, Limeng; Qu, Yang; Wang, Shuo; Liu, Yutu; Yang, Ranran; Ren, Xing; Ma, Yuze

doi:10.1016/j.egyr.2024.03.046

Cited by 3 publications

(1 citation statement)

References 36 publications

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“…Furthermore, LSTM contain several key hyperparameters, and it is not easy to manually find the ideal parameter configuration for a specific task; Therefore, it is particularly important to find a suitable optimization method. Wang et al proposed a load prediction method based on particle swarm optimization LSTM, and proposed a suitable energy storage scheme according to the prediction results [15]. Based on the clone optimization algorithm, ALI AL BATAINEH selected the optimal LSTM network topology and parameter configuration, innovatively solved many text classification tasks, and finally achieved good performance [16].…”

Section: Introductionmentioning

confidence: 99%

High-precision concentration detection of CO₂ in flue gas based on BO-LSTM and variational mode decomposition

Wang,

Chen,

Kong

et al. 2024

Meas. Sci. Technol.

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In order to improve the detection accuracy of CO2 and other gases in the flue gas emitted from thermal power plants, a CO2 concentration detection model based on tunable semiconductor laser absorption spectroscopy was proposed. First, a variational mode decomposition model was used to filter the harmonic signal after removing the outliers to reduce the influence of noise on the detection results. Suitable absorption lines and concentration characteristics were then selected according to the gas absorption properties and correlation theory. Finally, the CO2 concentration inversion was completed using long short-term memory networks, and a Bayesian optimization algorithm was introduced to optimize the hyperparameters of the network. The experimental results showed that the R 2 and RMSE of the test set were 0.998 84 and 0.116 08, respectively, in the concentration range of 1%–12%. In addition, the Allan analysis of variance revealed that the maximum measurement error of CO2 was only 0.005 619% when the integration time was 38 s. Compared to the traditional CO2 detection schemes, the detection accuracy and stability are significantly improved, which provides a feasible scheme for flue gas detection in thermal power plants.

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Section: Introductionmentioning

confidence: 99%

High-precision concentration detection of CO₂ in flue gas based on BO-LSTM and variational mode decomposition

Wang,

Chen,

Kong

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

Transfer-learning enabled adaptive framework for load forecasting under concept-drift challenges in smart-grids across different-generation-modalities

Azeem,

Ismail,

Jameel

et al. 2024

Energy Reports

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Research on Ultra-short-term combination forecasting algorithm of power load based on machine learning

Gao,

Wang,

Kang

et al. 2024

J. Phys.: Conf. Ser.

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Power load forecasting is of great significance to the power grid marketing department. To obtain accurate load forecasting results, a minute-by-minute forecasting method for electricity load based on multi-stage is proposed (TPE-WXL) by combining the non-linear and time-series attributes. Firstly, the historical series of specific areas in the city are pre-processed. Then, in order to obtain accurately predicted results, XGBoost and LightGBM are applied to extract attributes from the series to build a hybrid model. Moreover, TPE is introduced to enhance the hyperparameters of the hybrid model and the attributes of the load series. Finally, the power load dataset of the region is used as an example to conduct experimental analysis. Experimental results revealed that the method can accurately forecast the trend of power load, that is, R2 =0.981, RMSE=2.643.

show abstract

Optimal allocation of customer energy storage based on power big data and improved LSTM load forecasting

Cited by 3 publications

References 36 publications

High-precision concentration detection of CO₂ in flue gas based on BO-LSTM and variational mode decomposition

High-precision concentration detection of CO₂ in flue gas based on BO-LSTM and variational mode decomposition

Transfer-learning enabled adaptive framework for load forecasting under concept-drift challenges in smart-grids across different-generation-modalities

Research on Ultra-short-term combination forecasting algorithm of power load based on machine learning

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Optimal allocation of customer energy storage based on power big data and improved LSTM load forecasting

Cited by 3 publications

References 36 publications

High-precision concentration detection of CO2 in flue gas based on BO-LSTM and variational mode decomposition

High-precision concentration detection of CO2 in flue gas based on BO-LSTM and variational mode decomposition

Transfer-learning enabled adaptive framework for load forecasting under concept-drift challenges in smart-grids across different-generation-modalities

Research on Ultra-short-term combination forecasting algorithm of power load based on machine learning

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Product

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About

High-precision concentration detection of CO₂ in flue gas based on BO-LSTM and variational mode decomposition

High-precision concentration detection of CO₂ in flue gas based on BO-LSTM and variational mode decomposition