A Deep Learning Methodology Based on Bidirectional Gated Recurrent Unit for Wind Power Prediction

Deng, Yaping; Hao, Jia; Li, Pengcheng; Tong, Xiangqian; Qiu, Xiaodong; Li, Feng

doi:10.1109/iciea.2019.8834205

Cited by 28 publications

(20 citation statements)

References 11 publications

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“…The comparison illustrated that SDAE has a more robust prediction ability to deal with nonlinear data. A bidirectional gated recurrent unit-based deep learning model demonstrated superior wind power forecasting in [13], and the results were verified using real data from a wind farm. Wang et al [14] introduced a deep belief network (DBN) on a multi-dimensional phase space to predict wind power.…”

Section: Introductionmentioning

confidence: 84%

Short-Term Wind-Speed Forecasting Based on Multiscale Mathematical Morphological Decomposition, K-Means Clustering, and Stacked Denoising Autoencoders

Dong

Sun

et al. 2020

IEEE Access

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Wind energy plays an increasingly important role in economic development. In this study, we propose a hybrid short-term wind-speed forecasting model comprising multiscale mathematical morphological decomposition (MMMD), K-means clustering algorithm, and stacked denoising autoencoder (SDAE) networks. First, in contrast to traditional signal-decomposing tools, the original wind-speed sequence is decomposed into a series of subsequences with different frequencies and fluctuant levels using the adaptive multiscale mathematical morphological algorithm directly in the time domain. The signal does not need to be transferred from the time domain to the frequency domain; hence, the accuracy can be considerably improved. Moreover, this is the first study that uses a time domain signal-decomposing tool in a hybrid wind forecasting model. Next, the data are split into different clusters of similar frequencies and fluctuant level subsequences using the K-means algorithm. The characteristics of each cluster are then captured using the SDAE as the core forecasting unit. Finally, the predictions of all subsequences are aggregated to obtain the final wind speed. The data from two real wind turbines are used to evaluate the performance of the proposed model, and the forecasting results are compared with five different benchmark models, namely, backpropagation neural network (BPNN), stacked denoising autoencoder (SDAE), mathematical morphology-backpropagation, mathematical morphology-SDAE, and K-means-SDAE for multiple scales, and two novel hybrid wind forecasting models namely, wavelet transform (WT)-K-means-SDAE and variation mode decomposition (VMD)-K-means-long short-term memory networks (LSTMs). The results of the comparison demonstrate that the proposed model provides a short-term wind-speed forecasting method whose prediction accuracy decreases with time; however, the proposed model achieves a better performance in comparison with other exiting models. At same time, the proposed model significantly increases the prediction accuracy of wind-speed forecasting and can be a reference for future research in this area. INDEX TERMS K-means clustering, multiscale mathematical morphological decomposition, short-term wind-speed forecasting, stacked denoising autoencoders.

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Section: Introductionmentioning

confidence: 84%

Short-Term Wind-Speed Forecasting Based on Multiscale Mathematical Morphological Decomposition, K-Means Clustering, and Stacked Denoising Autoencoders

Dong

Sun

et al. 2020

IEEE Access

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“…Results indicated that stacked denoising auto-encoder deep learning model predicts accurate long-term temperature Sequence to sequence weather forecasting with long short-term memory recurrent neural networks [36] Multi-stacked LSTMs are used to map sequences of weather values of the same length. Use three input parameters and predict one parameter at a time A deep learning methodology based on bidirectional gated recurrent unit for wind power prediction [62] Contributed the bidirectional gated recurrent network for wind power forecasting. The model used wind direction and wind speed as inputs and predicted the results more accurately up to 6 h post-processing software.…”

Section: Weather Research and Forecasting (Wrf) Modelmentioning

confidence: 99%

“…Therefore, proposed models for weather forecasting are MIMO-LSTM, MISO-LSTM, MIMO-TCN, and MISO-TCN. Deep learning models are discussed in [11,34,39,61,62] are single input single output models. The MISO are experimented in [35,36] and a MIMO is discussed in [63].…”

Section: Proposed Model For Weather Forecastingmentioning

confidence: 99%

“…Moreover, [62] uses the bidirectional recurrent network with weather-related input parameters successfully to predict the wind power up to 6 h. Therefore, bidirectional LSTM experiments in long-term forecasting and compare with the proposed model. Most of the researches discussed in Table 1 are attempted to forecasting a single or few parameters for a specific purpose rather developing a complete weather forecasting model.…”

Section: Proposed Model For Weather Forecastingmentioning

confidence: 99%

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Deep learning-based effective fine-grained weather forecasting model

Hewage

Trovati

Pereira

et al. 2020

Pattern Anal Applic

146

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It is well-known that numerical weather prediction (NWP) models require considerable computer power to solve complex mathematical equations to obtain a forecast based on current weather conditions. In this article, we propose a novel lightweight data-driven weather forecasting model by exploring temporal modelling approaches of long short-term memory (LSTM) and temporal convolutional networks (TCN) and compare its performance with the existing classical machine learning approaches, statistical forecasting approaches, and a dynamic ensemble method, as well as the well-established weather research and forecasting (WRF) NWP model. More specifically Standard Regression (SR), Support Vector Regression (SVR), and Random Forest (RF) are implemented as the classical machine learning approaches, and Autoregressive Integrated Moving Average (ARIMA), Vector Auto Regression (VAR), and Vector Error Correction Model (VECM) are implemented as the statistical forecasting approaches. Furthermore, Arbitrage of Forecasting Expert (AFE) is implemented as the dynamic ensemble method in this article. Weather information is captured by time-series data and thus, we explore the state-of-art LSTM and TCN models, which is a specialised form of neural network for weather prediction. The proposed deep model consists of a number of layers that use surface weather parameters over a given period of time for weather forecasting. The proposed deep learning networks with LSTM and TCN layers are assessed in two different regressions, namely multi-input multi-output and multi-input single-output. Our experiment shows that the proposed lightweight model produces better results compared to the well-known and complex WRF model, demonstrating its potential for efficient and accurate weather forecasting up to 12 h.

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“…Deng et al proposed a deep learning framework based on bidirectional gated recurrent unit for wind power prediction to improve the accuracy by making full use of the information provided by multiple data sources of numerical weather forecast. Results show bidirectional model helps to further improve prediction accuracy [23]. Atef et al conduct a systematic experimental methodology to investigate the impact of using deep-stacked unidirectional and bidirectional networks on predicting electricity load consumption and draw a conclusion that bidirectional models have significant improvement in the prediction accuracy while they consume almost twice the time of the unidirectional models [24].…”

Section: Literature Reviewmentioning

confidence: 99%

Short-Term Load Forecasting Using Recurrent Neural Networks With Input Attention Mechanism and Hidden Connection Mechanism

Zhang

2020

IEEE Access

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Short-term load forecasting is a critical task in the smart grid, which can be used to optimize power deployment and reduce power losses. Recurrent neural networks (RNNs) are the most popular deep learning models for short-term load forecasting. However, despite of achieving better forecasting accuracy than the traditional models, the performance of the existing RNN-based load forecasting approaches is still unsatisfactory for practical usage. Therefore, in this work, we have proposed input attention mechanism (IAM) and hidden connection mechanism (HCM) to greatly enhance the accuracy and efficiency of RNNbased load forecasting models. Specifically, we use IAM to assign the importance weights on input layers, which have better performances in both efficiency and accuracy than traditional attention mechanisms. To further enhance the models' efficiency, HCM is then applied to utilize residual connections to enhance the model's converging speed. We have applied both IAM and HCM on four state-of-the-art RNN implementations, and then conducted extensive experimental studies on two public datasets. Experimental results show that the proposed RNNs with IAM and HCM models achieve much better performances than the state-of-the-art baselines in both accuracy and efficiency. Ablation studies show that both IAM and HCM are essential to achieve such superior performances.

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A Deep Learning Methodology Based on Bidirectional Gated Recurrent Unit for Wind Power Prediction

Cited by 28 publications

References 11 publications

Short-Term Wind-Speed Forecasting Based on Multiscale Mathematical Morphological Decomposition, K-Means Clustering, and Stacked Denoising Autoencoders

Short-Term Wind-Speed Forecasting Based on Multiscale Mathematical Morphological Decomposition, K-Means Clustering, and Stacked Denoising Autoencoders

Deep learning-based effective fine-grained weather forecasting model

Short-Term Load Forecasting Using Recurrent Neural Networks With Input Attention Mechanism and Hidden Connection Mechanism

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