A Survey of Deep Learning Techniques: Application in Wind and Solar Energy Resources

Abstract

Nowadays, learning-based modeling system is adopted to establish an accurate prediction model for renewable energy resources. Computational Intelligence (CI) methods have become significant tools in production and optimization of renewable energies. The complexity of this type of energy lies in its coverage of large volumes of data and variables which have to be analyzed carefully. The present study discusses different types of Deep Learning (DL) algorithms applied in the field of solar and wind energy resources and evaluates their performance through a novel taxonomy. It also presents a comprehensive state-of-the-art of the literature leading to an assessment and performance evaluation of DL techniques as well as a discussion about major challenges and opportunities for comprehensive research. Based on results, differences on accuracy, robustness, precision values as well as the generalization ability are the most common challenges for the employment of DL techniques. In case of big dataset, the performance of DL techniques is significantly higher than that for other CI techniques. However, using and developing hybrid DL techniques with other optimization techniques in order to improve and optimize the structure of the techniques is preferably emphasized. In all cases, hybrid networks have better performance compared with single networks, because hybrid techniques take the advantages of two or more methods for preparing an accurate prediction. It is recommended to use hybrid methods in DL techniques. INDEX TERMS Big dataset, deep learning, modeling, optimizing, solar energy, wind energy. ACRONYMS USED FREQUENTLY IN THIS WORK GHG Greenhouse gas LSTM Long short-term memory Network FL Fuzzy logic SAE Stacked auto-encoder DL Deep learning DRL Deep reinforcement learning CI Computational intelligent WNN wavelet neural network DBN Deep belief network DRWNN diagonal recurrent wavelet neural network RBM Restricted Boltzmann machine ANFIS Adaptive neuro fuzzy inference system The associate editor coordinating the review of this manuscript and approving it for publication was Ton Do. RBF Radial basis function EC Evolutionary computation CNN Convolutional neural network MLP Multi layered perceptron TDNN Time delay neural network NARNN Nonlinear auto regressive neural network FFNN Feed-forward neural network CPRS Continuous ranking probability score (ANNs Artificial neural networks SVM Support vector machine MRBM Multilayer Restricted Boltzmann Machine BPNN Back Propagation Neural Network WT Wavelet transform QR Quintile regression

Economic Feasibility of Floating Offshore Wind Farms in the North of Spain

“…In fact, the perspectives of the Wind Europe, for Spain, for 2020 in terms of offshore wind is only 5 MW [7], which is the power of an offshore wind turbine installed in a port in the Canary islands to be tested as offshore wind turbine, but still not deployed at sea. Therefore, it is important to boost the offshore wind sector in the future [8] in order to achieve the success that can be found in the onshore wind sector, including its operational issues such as the safety [9] availability [10,11] and maintenance [12,13], its integration in the grid [14], its offshore wind resource [15][16][17][18], its layout optimization [19], or its resource prediction [20].…”

Open-Circuit Fault Diagnosis of Wind Power Converter Using Variational Mode Decomposition, Trend Feature Analysis and Deep Belief Network

“…The capacity of power converters in recent years has steadily grown in step with the increased size of large wind turbines; correspondingly, the load capacity of components of the converter have improved evidently and the electrical structure is more complex, which is bound to raise the failure rate greatly [1,2]. Meanwhile, the wind farms are mostly built in areas with abundant wind resources and complicated climate [3,4]. The operational environmentof the power converter is extremely harsh, and the failure rate is high [5].…”

Open-Circuit Fault Diagnosis of Wind Power Converter Using Variational Mode Decomposition, Trend Feature Analysis and Deep Belief Network

“…The capacity of power converter in recent years has steadily grown in step with increased size of large wind turbines, correspondingly, the load capacity of components of converter improved evidently and the electrical structure is more complex, which is bound to raise the failure rate greatly [1,2]. Meanwhile, the wind farms are mostly built in areas with abundant wind resources and complex weather climate [3,4]. The operational ambient of power converter is extremely harsh, and the failure rate is high [5].…”