A New Wind Power Forecasting Algorithm Based on Spark
- https://doi.org/10.2991/icmmita-15.2015.101How to use a DOI?
- Wind Power Prediction; Spark; Parallelization Improvement; Artificial Intelligence
With the arrival of the era of big data, wind power forecasting data develops to the massive and multi-dimensional direction. Single computer lack of resources is difficult to solve the problem of wind power forecasting. In this paper, Spark cloud computing platform is introduced to solve the massive data problem that the traditional single machine can not deal with. The combination of BP neural network with the bacterial colony optimization algorithm can avoid the local optimum and improve the accuracy of forecasting results. In the experiment?the real data of a wind farm in Nei Mongol is used as the input data set and Spark cloud computing programming framework improve the parallel algorithm. The results show that the improved algorithm can increase the efficiency of prediction results about 10%.
- © 2015, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Shaomin Zhang AU - Peng Chen AU - Baoyi Wang PY - 2015/11 DA - 2015/11 TI - A New Wind Power Forecasting Algorithm Based on Spark BT - Proceedings of the 2015 3rd International Conference on Machinery, Materials and Information Technology Applications PB - Atlantis Press SP - 520 EP - 525 SN - 2352-538X UR - https://doi.org/10.2991/icmmita-15.2015.101 DO - https://doi.org/10.2991/icmmita-15.2015.101 ID - Zhang2015/11 ER -