Research of short-term load forecasting based on combined grey neural network and phase space reconstruction

According to the characteristics of grey theory, G-P algorithm of phase space reconstruction and artificial neural network (ANN), a combined algorithm (G-G-NN) is proposed. The original time series is transformed by accumulated generating of grey prediction and G-P algorithm of phase space reconstru...

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Bibliographic Details
Published in2009 International Conference on Machine Learning and Cybernetics Vol. 2; pp. 1194 - 1199
Main Authors Shuo-He Wang, Rui-Lin Hao, Yu-Jian Chang, Yao Zhao
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2009
Subjects
Artificial neural networks
Clustering algorithms
Cybernetics
Electric power systems
G-G-NN algorithm
G-P algorithm
Grey theory
Load forecasting
Machine learning
Neural network
Neural networks
Power system modeling
Prediction algorithms
Predictive models
Short-term load forecasting
Steel
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ISBN9781424437023
1424437024
ISSN2160-133X
DOI10.1109/ICMLC.2009.5212444

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Summary:According to the characteristics of grey theory, G-P algorithm of phase space reconstruction and artificial neural network (ANN), a combined algorithm (G-G-NN) is proposed. The original time series is transformed by accumulated generating of grey prediction and G-P algorithm of phase space reconstruction. When a regular time series phase space is generated, neural network is adopted to forecast. The practical example indicated that the algorithm is verified.
ISBN:9781424437023
1424437024
ISSN:2160-133X
DOI:10.1109/ICMLC.2009.5212444