Wind Power Prediction Based on Principal Component Analysis and Long Short-Term Memory Networks

Lang, W. Paul; Ma, Xiangjin; Luo, Yanhong; Zhou, Bowen; Yang, Dongsheng

doi:10.1109/isgt-asia.2019.8881062

Cited by 3 publications

(3 citation statements)

References 26 publications

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“…If the bandwidth is too large, it will cause the fitted PDF to be too smooth and deviate from the true distribution. If the bandwidth is too small, the fitted PDF is susceptible to single-point values and may experience significant fluctuations [41]. Therefore, bandwidth optimization is necessary for KDE.…”

Section: A Methods Based On An Adaptive Bandwidthmentioning

confidence: 99%

Research on a Deep Ensemble Learning Model for the Ultra-Short-Term Probabilistic Prediction of Wind Power

Zhou,

Wei,

Kuang

et al. 2024

Electronics

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An accurate method for predicting wind power is crucial in effectively mitigating wind energy fluctuations and ensuring a stable power supply. Nevertheless, the inadequacy of the stability of wind energy severely hampers the consistent functioning of the power grid and the reliable provision of electricity. To enhance the accuracy of wind power forecasting, this paper proposes an ensemble model named the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and convolutional bidirectional long short-term memory (CNN-BiLSTM), which incorporates a data preprocessing technique, feature selection method, deep ensemble model, and adaptive control. Initially, CEEMDAN is utilized to decompose wind speed and power sequences and hence obtain decomposed subsequences for further analysis. Subsequently, the CNN is used to extract features from each subsequence, whereas each subsequence is processed by BiLSTM to obtain an ultra-short-term deterministic prediction model. Additionally, the adaptive kernel density estimation (AKDE) method is employed to estimate the probabilistic distribution of prediction error, enabling ultra-short-term probabilistic wind power prediction. Finally, based on real datasets, the reliability of the model in probabilistic prediction is verified through the evaluation metrics of multi-step prediction intervals (PIs).

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Section: A Methods Based On An Adaptive Bandwidthmentioning

confidence: 99%

Research on a Deep Ensemble Learning Model for the Ultra-Short-Term Probabilistic Prediction of Wind Power

Zhou,

Wei,

Kuang

et al. 2024

Electronics

View full text Add to dashboard Cite

show abstract

“…The first layer of neurons is the forgetting gate control layer, which helps LSTM decide to delete some information in the memory cell state, as shown in Formula (7).…”

Section: Network Model Of Long-and Short-term Memorymentioning

confidence: 99%

“…where As a deep neural network with memory ability, LSTM has been widely used in nonlinear sequence prediction. Compared with a static neural network, LSTM can better explore the correlation between sequence data and is very suitable for dealing with problems highly related to time series [7,8].…”

Section: Network Model Of Long-and Short-term Memorymentioning

confidence: 99%

Optimal Regulation of Parallel Closing Angle of Power System Loop Network Considering Wind–Wave Dynamics

et al. 2022

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The shortage of fossil fuels has led to increasing attention on new energy technologies, such as wind power and photovoltaic energy technologies, and the volatility of new energy has become the biggest obstacle for its participation in the process of power-system restoration. This paper presents an optimal control method of the parallel closing angle of the loop network in the process of power system recovery considering the uncertainty of wind-power output. Firstly, based on solving the uncertainty of wind-power output, a probability-prediction model of wind-power output based on the quantile regression of long-term and short-term memory networks (LSTMs) is established. Based on predicting the future wind-power output interval, the probability density function of the output at each time point in the future is obtained by a kernel density estimation. Secondly, by adjusting the output of conventional units and restoring the feeder load, a multiobjective optimal-control model is established to minimize the output change of conventional units and restore the most important feeder load. Based on considering the output probability of new energy, the optimal control of the phase angle difference at both ends of the line to be paralleled is realized, and the multiobjective optimization algorithm is used to solve the established model. Finally, the effectiveness of the proposed method and model is verified by IEEE 39-bus system simulation.

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Adaptation of a Real-Time Deep Learning Approach With an Analog Fault Detection Technique for Reliability Forecasting of Capacitor Banks Used in Mobile Vehicles

et al. 2022

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The reliability of DC-link capacitor-banks (CBs) encounters many challenges due to their usage in electric vehicles. Heavy shocks may damage the internal capacitors without shutting down the CB. The fundamental development obstacles of CBs are: lack of considering capacitor degradation in reliability assessment, the impact of unforeseen sudden internal capacitor faults in forecasting CB lifetime, and the faults consequence on CB degradation. The sudden faults change the CB capacitance, which leads to reliability change. To more accurately estimate the reliability, the type of the fault needs to be detected for predicting the correct post-fault capacitance. To address these practical problems, a new CB model and reliability assessment formula covering all fault types are first presented, then, a new analog fault-detection method is presented, and a combination of online-learning long short-term memory (LSTM) and faultdetection method is subsequently performed, which adapt the sudden internal CB faults with the LSTM to correctly predict the CB degradation. To confirm the correct LSTM operation, four capacitors degradation is practically recorded for 2000-hours, and the off-line faultless degradation values predicted by the LSTM are compared with the actual data. The experimental findings validate the applicability of the proposed method. All experimental codes-data are attached.

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Wind Power Prediction Based on Principal Component Analysis and Long Short-Term Memory Networks

Cited by 3 publications

References 26 publications

Research on a Deep Ensemble Learning Model for the Ultra-Short-Term Probabilistic Prediction of Wind Power

Research on a Deep Ensemble Learning Model for the Ultra-Short-Term Probabilistic Prediction of Wind Power

Optimal Regulation of Parallel Closing Angle of Power System Loop Network Considering Wind–Wave Dynamics

Adaptation of a Real-Time Deep Learning Approach With an Analog Fault Detection Technique for Reliability Forecasting of Capacitor Banks Used in Mobile Vehicles

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