Forecasting electricity demand by hybrid machine learning model

Authors:
Shu Fan;Chengxiong Mao;Jiadong Zhang;Luonan Chen
Affiliations:
Osaka Sangyo University, Daito, Osaka, Japan;Huazhong University of Science and Technology, Wuhan, China;Osaka Sangyo University, Daito, Osaka, Japan;Osaka Sangyo University, Daito, Osaka, Japan
Venue:
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Year:
2006

Citing 7
Cited 0

Support-Vector Networks

Machine Learning
An introduction to support Vector Machines: and other kernel-based learning methods

An introduction to support Vector Machines: and other kernel-based learning methods
Time Series Analysis, Forecasting and Control

Time Series Analysis, Forecasting and Control
Bayesian Clustering by Dynamics

Machine Learning - Special issue: Unsupervised learning
Clustering Continuous Time Series

ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
LIBSVM: A library for support vector machines

ACM Transactions on Intelligent Systems and Technology (TIST)
Peak load forecasting using the self-organizing map

ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper proposes a hybrid machine learning model for electricity demand forecasting, based on Bayesian Clustering by Dynamics (BCD) and Support Vector Machine (SVM). In the proposed model, a BCD classifier is firstly applied to cluster the input data set into several subsets by the dynamics of load series in an unsupervised manner, and then, groups of 24 SVMs for the next day's electricity demand curve are used to fit the training data of each subset. In the numerical experiment, the proposed model has been trained and tested on the data of the historical load from New York City.