Short-term prediction of wind power based on BiLSTM–CNN–WGAN-GP

Huang, Ling; Li, Linxia; Wei, Xiaoyuan; Zhang, Dongsheng

doi:10.1007/s00500-021-06725-x

Cited by 22 publications

(27 citation statements)

References 31 publications

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“…In this paper, the performance evaluation of proposed HPMI-DQR, and existing various algorithms such as EEMD-CSO-LSTMEFG [1], VMD-K means-LSTM [2], EALSTM-QR [22], and LCWGAN-GP [28] are compared.…”

Section: Resultsmentioning

confidence: 99%

“…Next, with the resultant features selected and provided as input to the deep learning model, robust wind power prediction is made. Te wind power prediction is employed for evaluating the expected production of one or more wind turbines and the proposed HPMI-DQR method has two existing methods such as EEMD-CSO-LSTMEFG [1], VMD-K means-LSTM [2], EALSTM-QR [22], and LCWGAN-GP [28] with the dataset of 10 minutes interval gap. From other methods and existing methods, a 10 minutes interval gap is applied.…”

Section: Methodsmentioning

confidence: 99%

“…In this section, the simulation of the proposed Homogenized Point Mutual Information and Deep Quantum Reinforced (HPMI-DQR) wind power prediction method and the three existing methods namely EEMD-CSO-LSTMEFG [1], VMD-K means-LSTM [2], EALSTM-QR [22], LCWGAN-GP [28] has implemented in Java programming language using 2018 SCADA data of a wind turbine in Turkey taken from the https://www.kaggle.com/berkerisen/wind-turbinescada-dataset [35]. Wind energy is considered to be technically feasible when its power density is than or equal to 500 W/m 2 , for a height equal to or exceeding 50 m above the ground, which requires a minimum wind speed between 7-8 m/s.…”

Section: Methodsmentioning

confidence: 99%

“…However, the proposed technique failed to explain the site selection issue. Short-term wind power prediction model (LCWGAN-GP) was discussed in [28] with higher prediction accuracy. But, the true positive rate was not measured.…”

Section: Introductionmentioning

confidence: 99%

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Homogenized Point Mutual Information and Deep Quantum Reinforced Wind Power Prediction

Jency

Judith

2022

International Transactions on Electrical Energy Systems

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Accurate wind power prediction is very predominant for genuine and effective power systems with high wind power perception. Wind power prediction, as well as wind power generation resources, receives the electrical energy by converting wind into rotational energy of the blades and converting rotational energy into electrical energy by the generator. Wind energy increases with the cube of wind speed. There are numerous common and deep learning methods that have evolved to attain wind power prediction. Deep learning-based methods are referred to as straightforward, and robust, and have been utilized in the recent few years for wind power prediction with a certain level of success. However, due to the lack of an appropriate feature selection process and to minimize the effect of losses used for wind power prediction, a large amount of computation is necessitated when processing multi-input wind power data, therefore causing a negative influence on scalability and hence affecting wind power prediction time. To address these issues, in this work, a method called, Homogenized Point Mutual Information and Deep Quantum Reinforced (HPMI-QDR) wind power prediction are proposed. The HPMI-DQR method is split into two sections. In the first section, informative and relevant features required for robust wind power prediction using input wind turbine data are designed using Homogenized Point Mutual (HPM) Feature Selection model. With the relevant features selected, in the second section, the actual wind power prediction is made using the Deep Quantum Reinforced Learning model. To validate the proposed method, Wind Turbine SCADA Dataset is used for constructing and testing. Simulation of proposed method attains enhancement within wind power prediction accuracy as 13%, minimal wind power prediction time as 25%, as well as better wind energy generation as 20% and true positive rate as 25%, compared using conventional techniques. Moreover, a substantial improvement was also found in wind power prediction time with minimum error.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Homogenized Point Mutual Information and Deep Quantum Reinforced Wind Power Prediction

Jency

Judith

2022

International Transactions on Electrical Energy Systems

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“…When choosing the options for increasing the power generation, the renewable energy source dominates others [1,2]. Renewable energy forecasting mainly solar and wind energies have gained a lot of attention currently due to its vital impact on taking proper operational and managerial decisions in power systems [3,4]. The permanency, grid reliability, reduction of cost and risk level in the energy market is contingent on the accuracy of wind energy prediction [5].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Wind Speed Forecasting Using Bi-LSTM with Selected Features

Subbiah¹,

Paramasivan²,

Karmel³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Wind speed forecasting is important for wind energy forecasting. In the modern era, the increase in energy demand can be managed effectively by forecasting the wind speed accurately. The main objective of this research is to improve the performance of wind speed forecasting by handling uncertainty, the curse of dimensionality, overfitting and non-linearity issues. The curse of dimensionality and overfitting issues are handled by using Boruta feature selection. The uncertainty and the non-linearity issues are addressed by using the deep learning based Bi-directional Long Short Term Memory (Bi-LSTM). In this paper, Bi-LSTM with Boruta feature selection named BFS-Bi-LSTM is proposed to improve the performance of wind speed forecasting. The model identifies relevant features for wind speed forecasting from the meteorological features using Boruta wrapper feature selection (BFS). Followed by Bi-LSTM predicts the wind speed by considering the wind speed from the past and future time steps. The proposed BFS-Bi-LSTM model is compared against Multilayer perceptron (MLP), MLP with Boruta (BFS-MLP), Long Short Term Memory (LSTM), LSTM with Boruta (BFS-LSTM) and Bi-LSTM in terms of Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Square Error (MSE) and R 2 . The BFS-Bi-LSTM surpassed other models by producing RMSE of 0.784, MAE of 0.530, MSE of 0.615 and R 2 of 0.8766. The experimental result shows that the BFS-Bi-LSTM produced better forecasting results compared to others.

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