Spatio-temporal wind power prediction using recurrent neural networks

TY - GEN

T1 - Spatio-temporal wind power prediction using recurrent neural networks

AU - Woon, Wei Lee

AU - Oehmcke, Stefan

AU - Kramer, Oliver

PY - 2017/1/1

Y1 - 2017/1/1

N2 - While wind is an abundant source of energy, integrating wind power into existing electricity grids is a major challenge due to its inherent variability. The ability to accurately predict future generation output would greatly mitigate this problem and is thus extremely valuable. Numerical Weather Prediction (NWP) techniques have been the basis of many wind prediction approaches, but the use of machine learning techniques is steadily gaining ground. Deep Learning (DL) is a sub-class of machine learning which has been particularly successful and is now the state of the art for a variety of classification and regression problems, notably image processing and natural language processing. In this paper, we demonstrate the use of Recurrent Neural Networks, a type of DL architecture, to extract patterns from the spatio-temporal information collected from neighboring turbines. These are used to generate short term wind energy forecasts which are then benchmarked against various prediction algorithms. The results show significant improvements over forecasts produced using state of the art algorithms.

AB - While wind is an abundant source of energy, integrating wind power into existing electricity grids is a major challenge due to its inherent variability. The ability to accurately predict future generation output would greatly mitigate this problem and is thus extremely valuable. Numerical Weather Prediction (NWP) techniques have been the basis of many wind prediction approaches, but the use of machine learning techniques is steadily gaining ground. Deep Learning (DL) is a sub-class of machine learning which has been particularly successful and is now the state of the art for a variety of classification and regression problems, notably image processing and natural language processing. In this paper, we demonstrate the use of Recurrent Neural Networks, a type of DL architecture, to extract patterns from the spatio-temporal information collected from neighboring turbines. These are used to generate short term wind energy forecasts which are then benchmarked against various prediction algorithms. The results show significant improvements over forecasts produced using state of the art algorithms.

UR - http://www.scopus.com/inward/record.url?scp=85035115382&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-70139-4_56

DO - 10.1007/978-3-319-70139-4_56

M3 - Article in proceedings

AN - SCOPUS:85035115382

SN - 9783319701387

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 556

EP - 563

BT - Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings

A2 - Zhao, Dongbin

A2 - Li, Yuanqing

A2 - El-Alfy, El-Sayed M.

A2 - Liu, Derong

A2 - Xie, Shengli

PB - Springer Verlag,

T2 - 24th International Conference on Neural Information Processing, ICONIP 2017

Y2 - 14 November 2017 through 18 November 2017

ER -