Wind Power Forecasting Based on Extended Latin Hypercube Sampling
- 10.2991/epee-16.2016.13How to use a DOI?
- distributed generation; wind power forecast; Latin hypercube sampling
With the rise of distributed generation, such as wind power and photovoltaic (PV), it is necessary to consider the effect of distributed generation's output randomness. Using the method of Latin hypercube sampling (LHS) can effectively fit output scenario. Considering the sampling number of conventional LHS (CLHS) must be fixed in advance, LHS(ELHS) can be Extended to predict wind power. The sample scenarios were extended exponentially on the basis of original scenarios by CLHS, taking the relative error of the output variation before and after the extension as the convergence criterion of ELHS. Numerical example results show the feasibility and accuracy of the proposed algorithm.
- © 2016, 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 - Xianbing Ding AU - Minfang Peng AU - Meie Shen AU - Liang Zhu AU - Hongwei Che AU - Sheng Zhou AU - Guangming Li AU - Rongsheng Liu PY - 2016/10 DA - 2016/10 TI - Wind Power Forecasting Based on Extended Latin Hypercube Sampling BT - Proceedings of the 2016 International Conference on Energy, Power and Electrical Engineering PB - Atlantis Press SP - 57 EP - 60 SN - 2352-5401 UR - https://doi.org/10.2991/epee-16.2016.13 DO - 10.2991/epee-16.2016.13 ID - Ding2016/10 ER -