Short-term prediction of the power of a new wind turbine based on IAO-LSTM

Li, Zheng; Luo, Xiaorui; Liu, Mengjie; Cao, Xin; Du, Shenhui; Sun, Hong

doi:10.1016/j.egyr.2022.07.030

Cited by 30 publications

(12 citation statements)

References 32 publications

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Section: Resultsmentioning

confidence: 99%

“…55,56 Wind power forecasting is a difficult task due to the influence of many factors, including weather, wind speed and temporal variability. 56…”

Section: Resultsmentioning

confidence: 99%

“…These results are consistent with other studies that have used LSTM modelling techniques to forecast wind power from turbines. 55,56 Wind power forecasting is a difficult task due to the influence of many factors, including weather, wind speed and temporal variability. 56 When integrating the LSTM models into a multi-objective optimisation framework aimed at maximising the CO 2 capture rate and minimising non-renewable power consumption, the observed model errors have important implications.…”

Section: Reaction Chemistry and Engineering Papermentioning

confidence: 99%

“…55,56 Wind power forecasting is a difficult task due to the influence of many factors, including weather, wind speed and temporal variability. 56 When integrating the LSTM models into a multi-objective optimisation framework aimed at maximising the CO 2 capture rate and minimising non-renewable power consumption, the observed model errors have important implications. The flue gas CO 2 LSTM model strong performance enables precise estimation of the CO 2 capture rate, contributing to effective decision-making for maximising the capture of CO 2 emissions from the coal plant.…”

Section: Reaction Chemistry and Engineering Papermentioning

confidence: 99%

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Responsive CO₂ capture: predictive multi-objective optimisation for managing intermittent flue gas and renewable energy supply

Fisher,

Xing,

Tian

et al. 2024

React. Chem. Eng.

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show abstract

Section: Resultsmentioning

confidence: 99%

“…55,56 Wind power forecasting is a difficult task due to the influence of many factors, including weather, wind speed and temporal variability. 56…”

Section: Resultsmentioning

confidence: 99%

Section: Reaction Chemistry and Engineering Papermentioning

confidence: 99%

Section: Reaction Chemistry and Engineering Papermentioning

confidence: 99%

See 2 more Smart Citations

Responsive CO₂ capture: predictive multi-objective optimisation for managing intermittent flue gas and renewable energy supply

Fisher,

Xing,

Tian

et al. 2024

React. Chem. Eng.

View full text Add to dashboard Cite

show abstract

“…The calculation is simple, but it is difficult to handle non-linear relationships, and the prediction accuracy is limited. Reference [11] proposed a wind power prediction method for different seasonal scenarios. A long short-term memory (LSTM) neural network prediction model with an improved Aquila optimizer was developed.…”

Section: Introductionmentioning

confidence: 99%

Short‐term prediction of wind power based on temporal convolutional network and the informer model

Wang,

Chang,

Liu

et al. 2023

IET Generation Trans & Dist

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In this study, a new short‐term wind power prediction model based on a temporal convolutional network (TCN) and the Informer model is proposed to solve the problem of low prediction accuracy caused by large wind speed fluctuations in short‐term prediction. First, an input feature selection method based on the maximum information coefficient is proposed after considering the problem of information interference caused by excessively large input features. A dynamic time planning method is used to select the optimal input step of historical power. Then, the combined forecasting model composed of TCN and the Informer is constructed in accordance with the numerical weather forecast and historical power data. Lastly, the pinball loss function is used to expand the prediction model into a quantile regression model, measure the effect of volatility, quantify the volatility range of prediction, and finally, obtain a deterministic prediction result. The actual measured data of wind farms in the Bohai Sea area are selected for analysis and calculation. The results show that the prediction model proposed in this study achieves better accuracy in deterministic prediction and interval prediction than the traditional model.

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Time Series Forecasting of Generated Power from Texas Wind Turbine

Antonijevic,

Hegedus,

Omolara

et al. 2024

Lecture Notes in Electrical Engineering

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Short-term prediction of the power of a new wind turbine based on IAO-LSTM

Cited by 30 publications

References 32 publications

Responsive CO₂ capture: predictive multi-objective optimisation for managing intermittent flue gas and renewable energy supply

Responsive CO₂ capture: predictive multi-objective optimisation for managing intermittent flue gas and renewable energy supply

Short‐term prediction of wind power based on temporal convolutional network and the informer model

Time Series Forecasting of Generated Power from Texas Wind Turbine

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Short-term prediction of the power of a new wind turbine based on IAO-LSTM

Cited by 30 publications

References 32 publications

Responsive CO2 capture: predictive multi-objective optimisation for managing intermittent flue gas and renewable energy supply

Responsive CO2 capture: predictive multi-objective optimisation for managing intermittent flue gas and renewable energy supply

Short‐term prediction of wind power based on temporal convolutional network and the informer model

Time Series Forecasting of Generated Power from Texas Wind Turbine

Contact Info

Product

Resources

About

Responsive CO₂ capture: predictive multi-objective optimisation for managing intermittent flue gas and renewable energy supply

Responsive CO₂ capture: predictive multi-objective optimisation for managing intermittent flue gas and renewable energy supply