skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Wind Speed Prediction with Spatio–Temporal Correlation: A Deep Learning Approach

Journal Article · · Energies
DOI:https://doi.org/10.3390/en11040705· OSTI ID:1474549
ORCiD logo [1];  [1];  [2];  [1];  [2]
  1. Huazhong Univ. of Science and Technology, Wuhan (China)
  2. Univ. of Tennessee, Knoxville, TN (United States)
+ Show Author Affiliations

Wind speed prediction with spatio–temporal correlation is among the most challenging tasks in wind speed prediction. In this paper, the problem of predicting wind speed for multiple sites simultaneously is investigated by using spatio–temporal correlation. This paper proposes a model for wind speed prediction with spatio–temporal correlation, i.e., the predictive deep convolutional neural network (PDCNN). The model is a unified framework, integrating convolutional neural networks (CNNs) and a multi-layer perceptron (MLP). Firstly, the spatial features are extracted by CNNs located at the bottom of the model. Then, the temporal dependencies among these extracted spatial features are captured by the MLP. In this way, the spatial and temporal correlations are captured by PDCNN intrinsically. Finally, PDCNN generates the predicted wind speed by using the learnt spatio–temporal correlations. In addition, three error indices are defined to evaluate the prediction accuracy of the model on the wind turbine array. Experiment results on real-world data show that PDCNN can capture the spatio–temporal correlation effectively, and it outperforms the conventional machine learning models, including multi-layer perceptron, support vector regressor, decision tree, etc.

Research Organization:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Organization:
USDOE
Grant/Contract Number:
AC05-00OR22725
OSTI ID:
1474549
Journal Information:
Energies, Vol. 11, Issue 4; ISSN 1996-1073
Publisher:
MDPI AGCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 69 works
Citation information provided by
Web of Science

References (39)

Short-term prediction of wind power with a clustering approach journal October 2010
Method selection in different regions for short-term wind speed prediction in Japan conference July 2015
Deep neural networks for ultra-short-term wind forecasting conference March 2015
An Efficient Approach to Power System Uncertainty Analysis With High-Dimensional Dependencies journal May 2018
Predictive Deep Boltzmann Machine for Multiperiod Wind Speed Forecasting journal October 2015
Deep Learning for Consumer Devices and Services: Pushing the limits for machine learning, artificial intelligence, and computer vision. journal April 2017
Deep belief network based deterministic and probabilistic wind speed forecasting approach journal November 2016
Statistical modelling of directional wind speeds using mixtures of von Mises distributions: Case study journal May 2008
An End-to-End Trainable Neural Network for Image-Based Sequence Recognition and Its Application to Scene Text Recognition journal November 2017
A Comparative Study of Empirical Mode Decomposition-Based Short-Term Wind Speed Forecasting Methods journal January 2015
A Fuzzy Model for Wind Speed Prediction and Power Generation in Wind Parks Using Spatial Correlation journal June 2004
Spatial prediction of wind farm outputs using the Augmented Kriging-based Model conference July 2012
A review of wind power and wind speed forecasting methods with different time horizons conference September 2010
The influence of wind direction on short-term wind power prediction: A case study in north China conference May 2012
Wind speed spatial estimation for energy planning in Sicily: Introduction and statistical analysis journal June 2008
The Wind Integration National Dataset (WIND) Toolkit journal August 2015
Learning Deep Architectures for AI journal January 2009
Analytical Assessment for Transient Stability Under Stochastic Continuous Disturbances journal March 2018
Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification journal March 2017
Correlation of Wind Speed Between Neighboring Measuring Stations journal June 2004
A method for coherency identification based on singular value decomposition conference July 2016
Transfer learning for short-term wind speed prediction with deep neural networks journal January 2016
Two Machine Learning Approaches for Short-Term Wind Speed Time-Series Prediction journal August 2016
Multi-Layer Interaction Graph for Analysis and Mitigation of Cascading Outages journal June 2017
Locally recurrent neural networks optimal filtering algorithms: application to wind speed prediction using spatial correlation conference January 2005
Optimizing the efficiency of deep learning through accelerator virtualization journal July 2017
A Review of Wind Power Forecasting Models journal January 2011
Wind Power Forecasts Using Gaussian Processes and Numerical Weather Prediction journal March 2014
Short-term prediction of the aggregated power output of wind farms—a statistical analysis of the reduction of the prediction error by spatial smoothing effects journal March 2002
Impact of Wind Forecast Error Statistics Upon Unit Commitment journal October 2012
Wind power prediction using deep neural network based meta regression and transfer learning journal September 2017
Injurious or Noninjurious Defect Identification From MFL Images in Pipeline Inspection Using Convolutional Neural Network journal July 2017
Pattern-Based Wind Speed Prediction Based on Generalized Principal Component Analysis journal July 2014
Impact of wind forecast error statistics upon unit commitment conference January 2013
Wind Integration National Dataset (WIND) Toolkit
  • Maclaurin, Galen; Draxl, Caroline; Hodge, Bri-Mathias
  • DOE Open Energy Data Initiative (OEDI); National Renewable Energy Laboratory https://doi.org/10.25984/1822195
dataset January 2014
An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition preprint January 2015
Schedulability Analysis of Conditional Parallel Task Graphs in Multicore Systems journal January 2016
Rough Deep Neural Architecture for Short-Term Wind Speed Forecasting journal December 2017
Input variable selection: mutual information and linear mixing measures journal January 2006

Cited By (2)

A Review of the Use of Artificial Neural Network Models for Energy and Reliability Prediction. A Study of the Solar PV, Hydraulic and Wind Energy Sources journal May 2019
Multi-Site Photovoltaic Forecasting Exploiting Space-Time Convolutional Neural Network journal November 2019

Similar Records

Related Subjects

17 WIND ENERGY