Research on Small Hydropower Generation Forecasting Method Based on Improved BP Neural Network
- DOI
- 10.2991/icmemtc-16.2016.214How to use a DOI?
- Keywords
- Correlation Analysis;Small hydropower forecast; BP network; Wavelet Decomposition
- Abstract
Small hydro-power generation shows strong uncertainty, which greatly affects the load forecasting work in small hydropower regions. Thus it's important to improve the accuracy of small hydropower generation load forecasting. At present, the most commonly used forecasting method is artificial neural network, which has strong adaptability and learning ability but poor generalization and easily falls into local minimum. The random fluctuation of small hydropower is not taken into consideration. This paper was based on analyzing the characteristics of small hydropower generation load, combining the wavelet transform to decompose the historical load to establish the prediction model for each component feature. Particle Swarm Optimization (Algorithm) was used to optimize initial weights and thresholds of neural networks before the prediction. After verified by real case in a rich small hydropower area in some province, the load prediction precision reaches 93.7%, higher than the precision of the high-voltage system criteria for assessing. The accuracy and effectiveness of the method is verified.
- Copyright
- © 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 - Miao Li AU - Chang Hong Deng AU - Jin Tan AU - Wei Yang AU - Li Zheng PY - 2016/04 DA - 2016/04 TI - Research on Small Hydropower Generation Forecasting Method Based on Improved BP Neural Network BT - Proceedings of the 2016 3rd International Conference on Materials Engineering, Manufacturing Technology and Control PB - Atlantis Press SP - 1085 EP - 1090 SN - 2352-5401 UR - https://doi.org/10.2991/icmemtc-16.2016.214 DO - 10.2991/icmemtc-16.2016.214 ID - Li2016/04 ER -