2016
DOI: 10.1016/j.apenergy.2016.08.108
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Deep belief network based deterministic and probabilistic wind speed forecasting approach

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Cited by 453 publications
(160 citation statements)
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“…Deep learning can automatically learn effective classification features from a large number of complex unlabeled data and has strong data classification and prediction capabilities (Schmidhuber, ). Deep brief network (DBN) was first proposed by Hinton in 2006 (Hinton & Salakhutdinov, ) and was extended to many areas such as speech recognition (Sarikaya, Hinton, & Ramabhadran, ; Zhu, Chen, Zhao, Zhou, & Zhang, ), handwriting number recognition (Hinton, ), plant leaf classification (Liu & Kan, ), and power load prediction (Dedinec, Filiposka, Dedinec, & Kocarev, ; Wang et al, ). Nowadays, some experts have introduced deep learning into spectroscopy.…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…Deep learning can automatically learn effective classification features from a large number of complex unlabeled data and has strong data classification and prediction capabilities (Schmidhuber, ). Deep brief network (DBN) was first proposed by Hinton in 2006 (Hinton & Salakhutdinov, ) and was extended to many areas such as speech recognition (Sarikaya, Hinton, & Ramabhadran, ; Zhu, Chen, Zhao, Zhou, & Zhang, ), handwriting number recognition (Hinton, ), plant leaf classification (Liu & Kan, ), and power load prediction (Dedinec, Filiposka, Dedinec, & Kocarev, ; Wang et al, ). Nowadays, some experts have introduced deep learning into spectroscopy.…”
Section: Introductionmentioning
confidence: 99%
“…Deep brief network (DBN) was first proposed by Hinton in 2006(Hinton & Salakhutdinov, 2006 and was extended to many areas such as speech recognition (Sarikaya, Hinton, & Ramabhadran, 2011;Zhu, Chen, Zhao, Zhou, & Zhang, 2017), handwriting number recognition (Hinton, 2009), plant leaf classification (Liu & Kan, 2016), and power load prediction (Dedinec, Filiposka, Dedinec, & Kocarev, 2016;Wang et al, 2016). Nowadays, some experts have introduced deep learning into spectroscopy.…”
Section: Introductionmentioning
confidence: 99%
“…Neural network approach; Support vector machine; Fuzzy and clustering approach [6,[15][16][17][18][19] Having a high ability of fault tolerance do not require accurate mathematical models with each man-machine interaction; Obtaining a satisfactory performance in non-linear time series forecasting [18] Easily getting into local optimum, over-fitting and exhibiting the relatively low convergence rate; Having a relatively low accuracy and lack for systematization [19].…”
Section: Artificial Intelligence Modelsmentioning
confidence: 99%
“…(1) y i2 y : (1) ith mean trend segment i into buckets, such that segments in the same bucket are similar. A bucket is chosen at random and partitioned into uniform widths of r. To increase the accuracy of index building, l hash table functions g 1 ðxÞ; g 2 ðxÞ; Á Á Á ; g l ðxÞ are applied, thus forming l tables H 1 $ H l .…”
Section: Locality Sensitive Hashingmentioning
confidence: 99%
“…As fossil fuels are gradually being depleted, wind energy, as a non-polluting type of renewable energy, has been rapidly developed [1]. In recent years, the proportion of wind energy keeps increasing and this trend will continue for a long time [2].…”
Section: Introductionmentioning
confidence: 99%