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/).

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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  -