A Deep Attention Convolutional Recurrent Network Assisted by K-Shape Clustering and Enhanced Memory for Short Term Wind Speed Predictions

Renewable energy resources can be deployed locally and efficiently using the concept of microgrids. Due to the natural uncertainty of the output power of renewable energy resources, the planning for a proper operation of microgrids can be a challenging task. In addition, the information about the loads and the power consumption of them can create benefits to increase the efficiency of the microgrids. However, electrical loads can have uncertainty due to reasons such as unpredictable behavior of the consumers. To exploit a microgrid, energy management is required at the upper level of operation and control in order to reduce the costs. One of the most important tasks of the energy management system is to satisfy the loads and, in other words, develop a plan to maintain equilibrium between the power generation and power consumption. To obtain information about the output power of renewable energy resources and power consumption, deep learning can be implemented as a powerful tool, which is able to predict the desired values. In addition, weather conditions can affect the output power of renewable energy-based resources and the behavior of the consumers and, as a result, the power consumption. So, deep learning can be deployed for the anticipation of the weather conditions. This paper will study the recent works related to deep learning, which has been implemented for the prediction of the output power of renewable energy resources (i.e., PVs and wind turbines), electrical loads, and weather conditions (i.e., solar irradiance and wind speed). In addition, for possible future directions some strategies are suggested, the most important of which is the implementation of quantum computing in cyber–physical microgrids.

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“…Additionally, for more studies, some methods have been proposed by [108][109][110][111][112][113][114][115] for the prediction of the wind speed.…”

Section: Wind Speed Predictionmentioning

confidence: 99%

Deep Learning for Forecasting-Based Applications in Cyber–Physical Microgrids: Recent Advances and Future Directions

et al. 2023

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“…[1][2][3][4] Traditional machine learning models, statistical models, and physical models are all employed for predicting. [5][6][7][8][9][10][11][12][13][14][15] With the development of deep learning and its application in WPP, deep learning models 16 including the convolutional neural networks (CNNs), [17][18][19][20][21] long short-term memory networks (LSTMs), [21][22][23][24][25] and a series of neural networks have attained more attention. Temporal convolutional network (TCN) performs superior to traditional CNN in time series.…”

Section: Introductionmentioning

confidence: 99%

“…With the development of deep learning and its application in WPP, deep learning models 16 including the convolutional neural networks (CNNs), 17–21 long short‐term memory networks (LSTMs), 21–25 and a series of neural networks have attained more attention. Temporal convolutional network (TCN) performs superior to traditional CNN in time series.…”

Section: Introductionmentioning

confidence: 99%

MTTLA‐DLW: Multi‐task TCN‐Bi‐LSTM transfer learning approach with dynamic loss weights based on feature correlations of the training samples for short‐term wind power prediction

Song,

Peng,

Song

et al. 2024

Wind Energy

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Wind power prediction for newly built wind farms is usually faced with the problem of no sufficient historical data. To efficiently extract the useful features from related wind farms, a novel transfer learning method based on temporal convolutional network (TCN)‐Bi‐long short‐term memory (LSTM) with dynamic loss weights is proposed. Firstly, a novel multi‐task TCN‐Bi‐LSTM model is designed to extract common features. The separate TCNs, and common Bi‐LSTM layers of the proposed model are designed to extract the temporal features from related wind farms. Secondly, in the pre‐training stage, to optimize the training process of the neural networks, a dynamic loss‐weighting strategy is proposed for multi‐task learning (MTL) to select the most related features, which increase the prediction accuracy by providing a suitable optimization object. Thirdly, the multi‐task TCN‐Bi‐LSTM model is re‐trained based on the samples from the target wind farm. Finally, a dataset of seven wind farms was employed to evaluate the efficiency of the proposed MTL structure and the dynamic loss‐weighting strategy. The result shows that the root mean squared error of the 12‐h short‐term prediction can be decreased by 4.19% compared with the traditional single‐task learning model, which verifies the validity of the proposed multi‐task transfer learning method.

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“…Zheng et al [13] proposed a capsule network (CapsNet) based on the spatio-temporal wind speed prediction method with improved prediction accuracy by extracting spatial information through a CNN and using a capsule network to extract temporal features. Yang et al [14] proposed a novel deep attention convolutional recurrent network (DACRN-KM). It added a Ktype clustering process and attention layer based on CNN and LSTM modules to better utilize the spatio-temporal information in wind speed data for accurate predictions.…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Wind Speed Prediction Based on Improved Residual Shrinkage Network

Liang

et al. 2023

Sustainability

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Considering the massive influx of new energy into the power system, accurate wind speed prediction is of great importance to its stability. Due to the influence of sensor accuracy and harsh natural environments, there is inevitable noise interference in original wind speed data, which adversely affects the accuracy of wind speed prediction. There are some problems associated with traditional signal processing methods when dealing with noise such as signal loss. We propose the use of a deep residual shrinkage unit based on soft activation (SDRSU) in order to reduce noise interference and ensure the integrity of original wind speed data. A deep network is constructed by stacking multiple SDRSUs to extract useful features from noisy data. Considering the spatio-temporal coupling relationship between wind turbines in a wind farm, a ST-SDRSN (soft-activation based deep spatio-temporal residual shrinkage network) will be used to model the wind speed series neighboring time property and daily periodic property. An accurate wind speed prediction can be achieved by extracting the spatial correlations between the turbines at each turbine along the time axis. We designed four depth models under the same spatio-temporal architecture to verify the advantages of the soft-activation block and the proposed ST-SDRSN model. Two datasets provided by the National Renewable Energy Laboratory (NREL) were used for our experiments. Based on different kinds of evaluation criteria in different datasets, ST-SDRSN was shown to improve prediction accuracy by 15.87%.

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A Deep Attention Convolutional Recurrent Network Assisted by K-Shape Clustering and Enhanced Memory for Short Term Wind Speed Predictions

Cited by 30 publications

References 40 publications

Deep Learning for Forecasting-Based Applications in Cyber–Physical Microgrids: Recent Advances and Future Directions

Deep Learning for Forecasting-Based Applications in Cyber–Physical Microgrids: Recent Advances and Future Directions

MTTLA‐DLW: Multi‐task TCN‐Bi‐LSTM transfer learning approach with dynamic loss weights based on feature correlations of the training samples for short‐term wind power prediction

Spatio-Temporal Wind Speed Prediction Based on Improved Residual Shrinkage Network

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