An Improved Load Forecasting Method Based on Optimal Weighted Combination

DOI

10.2991/lemcs-14.2014.66How to use a DOI?

Keywords

Load forecasting; grey model; BP neural network;additional momentum-adaptive learning rate;optimal weighted combination

Abstract

Load forecasting is the basis for the planning and safe operation of electric power. To satisfy all forecasting conditions as much as possible and improve the results of load forecasting, the combination forecasting method is proposed. The new method integrates the advantages of residual gray theoretical model and back propagation (BP for short) neural network model with additional momentum-adaptive learning rate. And the weighted coefficient optimization process is introduced in the study. Basing on the electricity consumption of 2001-2011of a power supply company, the optimal weighted combination method are applied to forecast and analyze. The simulation results show that the mean square error of optimal weighted combination forecasting method is smaller than that of either a single forecasting method, which can improve the prediction precision significantly.

Copyright

© 2014, 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  - Chun Zhang
AU  - Hao Mei
AU  - Feng Li
PY  - 2014/05
DA  - 2014/05
TI  - An Improved Load Forecasting Method Based on Optimal Weighted Combination
BT  - Proceedings of the International Conference on Logistics, Engineering, Management and Computer Science
PB  - Atlantis Press
SP  - 281
EP  - 285
SN  - 1951-6851
UR  - https://doi.org/10.2991/lemcs-14.2014.66
DO  - 10.2991/lemcs-14.2014.66
ID  - Zhang2014/05
ER  -