New Hybrid Deep Neural Architectural Search-Based Ensemble Reinforcement Learning Strategy for Wind Power Forecasting

Jalali, Seyed Mohammad Jafar; Osório, Gerardo J.; Ahmadian, Sajad; Lotfi, Mohamed; Campos, Vasco; Shafie‐khah, Miadreza; Khosravi, Abbas; Catalào, João P. S.

doi:10.1109/tia.2021.3126272

Cited by 36 publications

(13 citation statements)

References 56 publications

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“…Notably, the reinforcement learning-based algorithm has shown great success in many real-world hyperparameter optimization problems, including hyperparameter tuning in controlled environments for deep learning network modelling [6,15,16,[26][27][28]. Many researchers have focused on neural architecture search (NAS) and hyperparameter optimization using reinforcement learning approaches.…”

Section: Reinforcement Learning Algorithmmentioning

confidence: 99%

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Deep-Learning Model Selection and Parameter Estimation from a Wind Power Farm in Taiwan

et al. 2022

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Deep learning networks (DLNs) use multilayer neural networks for multiclass classification that exhibit better results in wind-power forecasting applications. However, improving the training process using proper parameter hyperisations and techniques, such as regularisation and Adam-based optimisation, remains a challenge in the design of DLNs for processing time-series data. Moreover, the most appropriate parameter for the DLN model is to solve the wind-power forecasting problem by considering the excess training algorithms, such as the optimiser, activation function, batch size, and dropout. Reinforcement learning (RN) schemes constitute a smart approach to explore the proper initial parameters for the developed DLN model, considering a balance between exploration and exploitation processes. Therefore, the present study focuses on determining the proper hyperparameters for DLN models using a Q-learning scheme for four developed models. To verify the effectiveness of the developed temporal convolution network (TCN) models, experiments with five different sets of initial parameters for the TCN model were determined by the output results of Q-learning computation. The experimental results showed that the TCN accuracy for 168 h wind power prediction reached a mean absolute percentage error of 1.41%. In evaluating the effectiveness of selection of hyperparameters for the proposed model, the performance of four DLN-based prediction models for power forecasting—TCN, long short-term memory (LSTM), recurrent neural network (RNN), and gated recurrence unit (GRU) models—were compared. The overall detection accuracy of the TCN model exhibited higher prediction accuracy compared to canonical recurrent networks (i.e., the GRU, LSTM, and RNN models).

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Section: Reinforcement Learning Algorithmmentioning

confidence: 99%

“…Later, Iranfar et al used reinforcement learning to tune the hyperparameters for a multi-layer perceptron (MLP) and CNN [27]. Jalali et al used an ensemble learning scheme with reinforcement learning strategy to increase the prediction accuracy of wind power forecasting [28].…”

Section: Reinforcement Learning Algorithmmentioning

confidence: 99%

Deep-Learning Model Selection and Parameter Estimation from a Wind Power Farm in Taiwan

et al. 2022

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“…They have good generalization ability and robustness and can provide more accurate wind power forecasting. In addition, deep learning is a machine learning concept that provides superior computation performance and flexibility by directly learning the best possible features of raw time series data; for example, the authors of [32][33][34][35][36][37][38][39][40][41][42][43][44] proposed novel data-driven models based on the concepts of deep learning-based convolutional-long short term memory (CLSTM), mutual information, evolutionary algorithm, neural architectural search procedure, and ensemble-based deep reinforcement learning (RL) strategies. The intention of hybrid model forecasting methods [20,24,36,[45][46] is to combine different forecasting models to increase the accuracy and precision of forecasts, with their main advantage being that they combine the advantages of each model used to provide the best forecast output.…”

Section: Introductionmentioning

confidence: 99%

A Review of Modern Wind Power Generation Forecasting Technologies

Tsai¹,

Hong²,

Lin³

et al. 2023

Preprint

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The prediction of wind power output is part of the basic work of power grid dispatching and energy distribution. At present, the output power prediction is mainly obtained by fitting and regressing the historical data. The medium- and long-term power prediction results exhibit large deviations due to the uncertainty of wind power generation. In order to meet the demand for accessing large-scale wind power into the electricity grid and to further improve the accuracy of short-term wind power prediction, it is necessary to develop models for accurate and precise short-term wind power prediction based on advanced algorithms for studying the output power of a wind power generation system. This paper summarizes the contribution of the current advanced wind power forecasting technology and delineates the key advantages and disadvantages of various wind power forecasting models. These models have different forecasting capabilities, update the weights of each model in real time, improve the comprehensive forecasting capability of the model, and have good application prospects in wind power generation forecasting. Furthermore, the case studies and examples in the literature for accurately predicting ultra-short-term and short-term wind power generation with uncertainty and randomness are reviewed and analyzed. Finally, we present prospects for future studies that can serve as useful directions for other researchers planning to conduct similar experiments and investigations.

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“…However, the performance of these algorithms in the field of computer vision is not sufficient. Therefore, deep learning is now widely employed in various industries [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 ], for example, to tackle problems in computer vision and succeed in image recognition. Deep learning techniques are used to assess complex and diverse pathological images.…”

Section: Introductionmentioning

confidence: 99%

Review of Semantic Segmentation of Medical Images Using Modified Architectures of UNET

AnbuDevi

Suganthi

2022

Diagnostics

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In biomedical image analysis, information about the location and appearance of tumors and lesions is indispensable to aid doctors in treating and identifying the severity of diseases. Therefore, it is essential to segment the tumors and lesions. MRI, CT, PET, ultrasound, and X-ray are the different imaging systems to obtain this information. The well-known semantic segmentation technique is used in medical image analysis to identify and label regions of images. The semantic segmentation aims to divide the images into regions with comparable characteristics, including intensity, homogeneity, and texture. UNET is the deep learning network that segments the critical features. However, UNETs basic architecture cannot accurately segment complex MRI images. This review introduces the modified and improved models of UNET suitable for increasing segmentation accuracy.

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New Hybrid Deep Neural Architectural Search-Based Ensemble Reinforcement Learning Strategy for Wind Power Forecasting

Cited by 36 publications

References 56 publications

Deep-Learning Model Selection and Parameter Estimation from a Wind Power Farm in Taiwan

Deep-Learning Model Selection and Parameter Estimation from a Wind Power Farm in Taiwan

A Review of Modern Wind Power Generation Forecasting Technologies

Review of Semantic Segmentation of Medical Images Using Modified Architectures of UNET

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