A novel algorithm for multi-node load forecasting based on big data of distribution network
Guangsong Hou, Ke Xu, Shoubin Yin, Yang Wang, Yan Han, Zhiguo Wang, Yifei Mao, Zhengxin Lei
Available Online November 2016.
- https://doi.org/10.2991/aest-16.2016.88How to use a DOI?
- Multi-Node Load Forecast; BP-ANN; AR; big data; distribution network.
- Effective load forecasting for different scales of loads is essential for the planning and operation of distribution network. The extending scale of the network, the diversity of load types and the rapid increase of data volume are some of the facing problems. In this paper, we propose a novel method for multi-node load forecasting, AR-ANN, which takes those problems into consideration. This new algorithm makes a combination of AR method and BP neural network method while eliminating their disadvantages. Comparing to traditional bottom-up method, a top-down method is more applicable when considering the limitation of measurement equipment in distribution network. Both top-down method and bottom-up method are tested in this paper by using AR-ANN algorithm. The data processing speed and the forecasting accuracy of AR-ANN is validated by several tests: an ordinary single-node load forecast, two multi-node load forecasts by traditional bottom-up method and by the new top-down method.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Guangsong Hou AU - Ke Xu AU - Shoubin Yin AU - Yang Wang AU - Yan Han AU - Zhiguo Wang AU - Yifei Mao AU - Zhengxin Lei PY - 2016/11 DA - 2016/11 TI - A novel algorithm for multi-node load forecasting based on big data of distribution network BT - 2016 International Conference on Advanced Electronic Science and Technology (AEST 2016) PB - Atlantis Press SP - 655 EP - 667 SN - 1951-6851 UR - https://doi.org/10.2991/aest-16.2016.88 DO - https://doi.org/10.2991/aest-16.2016.88 ID - Hou2016/11 ER -