Journal ArticleDOI
A new design methodology to predict wind farm energy production by means of a spiking neural network-based system
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This article is published in International Journal of Numerical Modelling-electronic Networks Devices and Fields.The article was published on 2019-07-01. It has received 30 citations till now. The article focuses on the topics: Wind power forecasting & Wind power.read more
Citations
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Blockchain, State-of-the-Art and Future Trends
TL;DR: A non exhaustive list on the state of the art about Blockchain technology in multiple application fields, both from an industry and business perspective and from a consumer one and a dedicated focus on the frictions between distributed ledgers and data protection regulations crossing all these areas are presented.
Journal ArticleDOI
Different Models for Forecasting Wind Power Generation: Case Study
David Barbosa de Alencar,Carolina M. Affonso,Roberto Célio Limão de Oliveira,Jorge Laureano Moya Rodríguez,Jandecy Cabral Leite,José Carlos Reston Filho +5 more
TL;DR: In this paper, the authors developed ultra-short, short, medium and long-term prediction models of wind speed, based on computational intelligence techniques, using artificial neural network models, Autoregressive Integrated Moving Average (ARIMA) and hybrid models including forecasting using wavelets.
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Wind Power Forecasting Based on Echo State Networks and Long Short-Term Memory
TL;DR: The experimental results show that LSTM+ESN using the autoencoder and quantile regression outperforms the WPPT model in all global metrics used.
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Wind speed forecasting system based on gated recurrent units and convolutional spiking neural networks
TL;DR: A hybrid dual temporal information wind speed forecasting system comprising a third-generation spiking neural network is proposed, aiming to better extract temporal information.
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Hourly day-ahead wind power forecasting with the EEMD-CSO-LSTM-EFG deep learning technique
TL;DR: An improved long short-term memory network-enhanced forget-gate network (LSTM-EFG) model, whose appropriate parameters are optimized using cuckoo search optimization algorithm (CSO), is used to forecast the subseries data that is extracted using ensemble empirical mode decomposition (EEMD).
References
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Collective dynamics of small-world networks
TL;DR: Simple models of networks that can be tuned through this middle ground: regular networks ‘rewired’ to introduce increasing amounts of disorder are explored, finding that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs.
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On Estimation of a Probability Density Function and Mode
TL;DR: In this paper, the problem of the estimation of a probability density function and of determining the mode of the probability function is discussed. Only estimates which are consistent and asymptotically normal are constructed.
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Synaptic Modifications in Cultured Hippocampal Neurons: Dependence on Spike Timing, Synaptic Strength, and Postsynaptic Cell Type
Guo-Qiang Bi,Mu-ming Poo +1 more
TL;DR: The results underscore the importance of precise spike timing, synaptic strength, and postsynaptic cell type in the activity-induced modification of central synapses and suggest that Hebb’s rule may need to incorporate a quantitative consideration of spike timing that reflects the narrow and asymmetric window for the induction of synaptic modification.
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Which model to use for cortical spiking neurons
TL;DR: The biological plausibility and computational efficiency of some of the most useful models of spiking and bursting neurons are discussed and their applicability to large-scale simulations of cortical neural networks is compared.
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Networks of spiking neurons: the third generation of neural network models
TL;DR: It is shown that networks of spiking neurons are, with regard to the number of neurons that are needed, computationally more powerful than other neural network models based on McCulloch Pitts neurons and sigmoidal gates.