Research on Smart Grid Load Forecasting Platform Based on Cloud Computing
- DOI
- 10.2991/asei-15.2015.282How to use a DOI?
- Keywords
- Cloud computing, smart grid, load forecasting
- Abstract
Along with the development of Smart Grid, a large number of intelligent terminal equipments are used in power grid. The environment, the means, and the targets of load forecasting will been greatly changed. The load forecasting systems have outstanding heterogeneous problems. Forecasting models cannot control the diversity and complexity, intermittent of the load. Forecasting process needs to weigh the rationality of the mathematical model and the real-time of the calculation frequently. And servers running are at low utilization. But load forecasting is highly required by Smart Grid Forecasting Load (SGFL). The development of load forecasting will enter a new stage, including mass data management of load forecasting, the emergence of fluctuating loads, and increasingly higher demand about the density and accuracy of load forecasting and so on, which have important impact on improving the level of load forecasting. With the emergence of cloud computing, power cloud generates. Cloud computing will be able to realize the mass data processing, and achieve reasonable resource allocation. Cloud computing has been widely used in medical, education, and electronic commerce, etc., and also successfully used in power system. Therefore, cloud computing will be adopted in SGLF in the near future.
- Copyright
- © 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 - Xian Chen AU - Bo Chen AU - Xiaozi Cui AU - Lin Liu PY - 2015/05 DA - 2015/05 TI - Research on Smart Grid Load Forecasting Platform Based on Cloud Computing BT - Proceedings of the 2015 International conference on Applied Science and Engineering Innovation PB - Atlantis Press SP - 1423 EP - 1426 SN - 2352-5401 UR - https://doi.org/10.2991/asei-15.2015.282 DO - 10.2991/asei-15.2015.282 ID - Chen2015/05 ER -