Fill Missing Data for Wind Farms Using Long Short-Term Memory Based Recurrent Neural Network

Li, Tie; Tang, Junci; Jiang, Feng; Xu, Xiaopeng; Li, Chun‐Zhu; Bai, Jiawen; Ding, Tao

doi:10.1109/cieec47146.2019.cieec-2019284

Cited by 7 publications

(3 citation statements)

References 23 publications

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“…Liu et al [25] used wavelet decomposition together with LSTM and found that it achieves better results than standard LSTM, but this comes at the cost of increased time complexity (training time increases by about 30%). Other studies on LSTM and wind power prediction have focused on tuning the LSTM architecture, for example, by testing different transformation functions [45] or by adding a specialized imputation module for missing data [22].…”

Section: Forecasting Models With Recurrent Neural Networkmentioning

confidence: 99%

Online learning of windmill time series using Long Short-term Cognitive Networks

Morales-Hernández¹,

Nápoles²,

Jastrzębska³

et al. 2021

Preprint

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Forecasting windmill time series is often the basis of other processes such as anomaly detection, health monitoring or maintenance scheduling. The amount of data generated on windmill farms makes online learning the most viable strategy to follow. Such settings require retraining the model each time a new batch of data is available. However, update the model with the new information is often very expensive to perform using traditional Recurrent Neural Networks (RNNs). In this paper, we use Long Short-term Cognitive Networks (LSTCNs) to forecast windmill time series in online settings. These recently introduced neural systems consist of chained Short-term Cognitive Network blocks, each processing a temporal data chunk. The learning algorithms of these blocks is based on a very fast, deterministic learning rule that makes LSTCNs suitable for online learning tasks. The numerical simulations using a case study with four windmills showed that our approach reported the lowest forecasting errors with respect to a simple RNN, a Long Short-term Memory, a Gated Recurrent Unit and a Hidden Markov Model. What is perhaps more important is that the LSTCN approach is significantly faster than these state-of-the-art models.

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Section: Forecasting Models With Recurrent Neural Networkmentioning

confidence: 99%

Online learning of windmill time series using Long Short-term Cognitive Networks

Morales-Hernández¹,

Nápoles²,

Jastrzębska³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…More recently, deep learning models, showing a more powerful ability in feature mining and data extraction, have been applied to data filling. In [10] a long short-term memory network (LSTM) was proposed to fill missing wind power data, which behaves better than the traditional NN method. In order to improve the reconstruction accuracy, an enhanced denoising autoencoder LSTM (EDAE-LSTM) model was proposed in [11], which is able to remove the noise, extract principle features of the dataset.…”

Section: Introductionmentioning

confidence: 99%

Dynamic State Estimation for Power System Based on the Measurement Data Reconstructed by RGAN

Pei

Sun

2021

IEEE Access

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Aiming at the problem of incomplete measurement data in the power system state estimation process, the missing data reconstructed by the residual generative adversarial network (RGAN) was introduced for the power system dynamic estimation. RGAN is a deep learning model based on the idea of a "two-player zero-sum" game, in which two deep neural networks compete with each other to mine the relevant features of the data. Distinguish from existing structure, skip connection and residual network (ResNet) were incorporated into two deep neural networks. And the incomplete measurement data were reconstructed accurately from the remaining measurement data. To lessen the impact of reconstruction errors, the unscented Kalman filter (UKF) method was used to estimate power system state. A case studied in the IEEE 30-bus shows that the UKF based RGAN dynamic estimation can maintain a high accuracy under different proportions of missing data.INDEX TERMS Incomplete measurement data, power system dynamic estimation, residual generative adversarial network, unscented Kalman filter.

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“…The second category mainly includes recurrent neural networks (RNNs), random forest, and back propagation (BP) neural networks [5], [6]. For example, the long short-term memory network is utilized as a predictor to fill the missing wind power data, which shows better performance than traditional RNNs in [7]. To deal with the problem of filling the large-scale missing wind power data, the improved random forest is proposed to combine the matrix combination, linear interpolation, and matrix transposition [8].…”

mentioning

confidence: 99%

Data-driven Missing Data Imputation for Wind Farms Using Context Encoder

Liao

Bak‐Jensen

Pillai

et al. 2022

Journal of Modern Power Systems and Clean Energy

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High-quality datasets are of paramount importance for the operation and planning of wind farms. However, the datasets collected by the supervisory control and data acquisition (SCADA) system may contain missing data due to various factors such as sensor failure and communication congestion. In this paper, a data-driven approach is proposed to fill the missing data of wind farms based on a context encoder (CE), which consists of an encoder, a decoder, and a discriminator. Through deep convolutional neural networks, the proposed method is able to automatically explore the complex nonlinear characteristics of the datasets that are difficult to be modeled explicitly. The proposed method can not only fully use the surrounding context information by the reconstructed loss, but also make filling data look real by the adversarial loss. In addition, the correlation among multiple missing attributes is taken into account by adjusting the format of input data. The simulation results show that CE performs better than traditional methods for the attributes of wind farms with hallmark characteristics such as large peaks, large valleys, and fast ramps. Moreover, the CE shows stronger generalization ability than traditional methods such as auto-encoder, K-means, k-nearest neighbor, back propagation neural network, cubic interpolation, and conditional generative adversarial network for different missing data scales.

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Fill Missing Data for Wind Farms Using Long Short-Term Memory Based Recurrent Neural Network

Cited by 7 publications

References 23 publications

Online learning of windmill time series using Long Short-term Cognitive Networks

Online learning of windmill time series using Long Short-term Cognitive Networks

Dynamic State Estimation for Power System Based on the Measurement Data Reconstructed by RGAN

Data-driven Missing Data Imputation for Wind Farms Using Context Encoder

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