Renewable energy – As future alternative for the Romanian rural communities

Cristina, Ada Flavia; Mănescu, C.; Dumitrescu, Carmen; Sirb, Nicoleta Mateoc

doi:10.1016/j.jbiotec.2015.06.364

Journal of Biotechnology

2015

DOI: 10.1016/j.jbiotec.2015.06.364

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Renewable energy – As future alternative for the Romanian rural communities

Ada Flavia Cristina

C. Mănescu

Carmen Dumitrescu

et al.

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“…This would reduce world-wide GHG emissions. Cristina [19] considered the potential of renewable energies as energy sources for electricity and heating uses in rural areas of Romania due to lack of electricity in some urban areas of this country. Renewable energy (green energy) could be a sustainable solution to living quality improvement in this area.…”

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

2019

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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

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

2019

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Renewable energy – As future alternative for the Romanian rural communities

Cited by 1 publication

References 0 publications

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

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

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