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Predictive Deep Boltzmann Machine for Multiperiod Wind Speed Forecasting
Zhang C.-Y.; Chen C.L.P.; Gan M.; Chen L.
2015
Source PublicationIEEE Transactions on Sustainable Energy
ISSN19493029
Volume6Issue:4Pages:1416
Abstract

It is important to forecast the wind speed for managing operations in wind power plants. However, wind speed prediction is extremely complex and difficult due to the volatility and deviation of the wind. As existing forecasting methods directly model the raw wind speed data, it is difficult for them to provide higher inference accuracy. Differently, this paper presents a sophisticated deep-learning technique for short-term and long-term wind speed forecast, i.e., the predictive deep Boltzmann machine (PDBM) and corresponding learning algorithm. The proposed deep model forecasts wind speed by analyzing the higher level features abstracted from lower level features of the wind speed data. These automatically learnt features are very informative and appropriate for the prediction. The proposed PDBM is a deep stochastic model that can represent the wind speed very well, and is inspired by two aspects. 1)The stochastic model is suitable to capture the probabilistic characteristics of wind speed. 2)Recent developments in neural networks with deep architectures show that deep generative models have competitive capability to approximate nonlinear and nonsmooth functions. The evaluation of the proposed PDBM model is depicted by both hour-ahead and day-ahead prediction experiments based on real wind speed datasets. The prediction accuracy of the PDBM model outperforms existing methods by more than 10%. © 2015 IEEE.

KeywordDeep Boltzmann Machine (Dbm) Deep Learning Time Series Wind Speed Prediction
DOI10.1109/TSTE.2015.2434387
URLView the original
Indexed BySCI
Language英语
WOS Research AreaScience & Technology - Other Topics ; Engineering ; Energy & Fuels
WOS SubjectGreen & Sustainable Science & Technology ; Energy & Fuels ; Engineering, Electrical & Electronic
WOS IDWOS:000361680800025
The Source to ArticleScopus
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Cited Times [WOS]:40   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Affiliation1.Univ Macau, Fac Sci & Technol, Dept Comp & Informat Sci, Macau 999078, Peoples R China
2.Hefei Univ Technol, Sch Elect Engn & Automat, Hefei 230009, Peoples R China
Recommended Citation
GB/T 7714
Zhang C.-Y.,Chen C.L.P.,Gan M.,et al. Predictive Deep Boltzmann Machine for Multiperiod Wind Speed Forecasting[J]. IEEE Transactions on Sustainable Energy,2015,6(4):1416.
APA Zhang C.-Y.,Chen C.L.P.,Gan M.,&Chen L..(2015).Predictive Deep Boltzmann Machine for Multiperiod Wind Speed Forecasting.IEEE Transactions on Sustainable Energy,6(4),1416.
MLA Zhang C.-Y.,et al."Predictive Deep Boltzmann Machine for Multiperiod Wind Speed Forecasting".IEEE Transactions on Sustainable Energy 6.4(2015):1416.
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