Medium and Long-term Power Load Forecasting based on the Thought of Big Data
- https://doi.org/10.2991/emcs-16.2016.324How to use a DOI?
- Load forecasting; Big data; Correlation; Model; Refineload forecasting; Big data; Correlation; Model; Refine
The traditional medium and long-term load forecasting methods are mainly carried out based upon model or algorithm, and forecasting results rely heavily on the accuracy of mathematical model, but model adaptability is very poor. Medium and long-term load forecasting lasts long and suffers from lots of uncertain influential factors in a broad spatial scope, so this paper proposes a big data technology-based medium and long-term load forecasting method. By analyzing the typical characteristics of the big data of load forecasting and the different levels of structure relations between the data, the paper sets up a big data system for load forecasting, a frame structure for load forecasting, and a big data-based medium and long-term refined load forecasting model, which falls into forecasting partition model and load forecasting model. The validity and practicability of this method is verified based on an analysis of the actual grid load in a certain region.
- © 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 - Xianzheng Feng AU - Tingting Zhang AU - Hongjun Li AU - Bin Zhao PY - 2016/01 DA - 2016/01 TI - Medium and Long-term Power Load Forecasting based on the Thought of Big Data BT - Proceedings of the 2016 International Conference on Education, Management, Computer and Society PB - Atlantis Press SP - 1312 EP - 1316 SN - 2352-538X UR - https://doi.org/10.2991/emcs-16.2016.324 DO - https://doi.org/10.2991/emcs-16.2016.324 ID - Feng2016/01 ER -