The Load Forecasting Model Design of Power System Based on Intelligent Algorithm

DOI

10.2991/ameii-16.2016.230How to use a DOI?

Keywords

Intelligent algorithm, power load, forecasting model, RBF neural network

Abstract

The paper aims at improving control of power plants on the system. Inaccurate predicting outcomes or oversize errors will impact fuel reasonable allocation of a power-generation department. In order to promote safe and economic operation of power grid, the paper analyzes power load forecasting, constructs a main framework of start grid load forecasting based on the cloud computing, uses fuzzy control theory to adjust and modify RBF neural network model, to improve the rate of convergence and to reduce training time, as well as establishes a load forecasting model of power system combining RBF neural network model and fuzzy control. Forecasting outcomes show that application of the model combining RBF neural network and fuzzy control generates minimum errors. The forecasting effects will be better, indicating the method has practice significance on load forecasting of power system.

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  - Han Han
PY  - 2016/04
DA  - 2016/04
TI  - The Load Forecasting Model Design of Power System Based on Intelligent Algorithm
BT  - Proceedings of the 2nd International Conference on Advances in Mechanical Engineering and Industrial Informatics (AMEII 2016)
PB  - Atlantis Press
SP  - 1217
EP  - 1223
SN  - 2352-5401
UR  - https://doi.org/10.2991/ameii-16.2016.230
DO  - 10.2991/ameii-16.2016.230
ID  - Han2016/04
ER  -