Proceedings of the 2017 3rd International Conference on Economics, Social Science, Arts, Education and Management Engineering (ESSAEME 2017)

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Energy Consumption Forecast Based on Coupling PSO-GPR

Authors
Xinli Wang, Shijian Liu, Liping Yan, Ning Wang
Corresponding Author
Xinli Wang
Available Online July 2017.
DOI
10.2991/essaeme-17.2017.414How to use a DOI?
Keywords
Gaussian Processes Regression, Particle Swarm Optimization, Prediction for energy consumption
Abstract

Consumption for energy has a great influence on sustainable development of economy and society. The scientific and rational energy development strategy can effectively guarantee social and economic stability and orderly development and energy development strategy is inseparable from the right to accurately predict energy demand and analysis. In order to improve the accuracy of prediction of energy consumption, hybrid forecasting model for energy consumption based on Gaussian process (GPR) and Particle Swarm (PSO) is proposed. Firstly, PSO algorithm is employed to optimize two parameters in covariance function, and then the initial value of parameters are obtained; next time series are trained in GPR model for energy consumption. Under the Bayesian framework, parameters in covariance function can be optimizing again. Finally, we can forecast energy consumption in well-trained GPR model, and the results can be compared with the auto-regressive integrated moving average model and exponential smoothing models. The results show that the proposed model has good stability and high prediction accuracy. It is suitable to be applied in forecasting consumption for energy.

Copyright
© 2017, 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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Volume Title
Proceedings of the 2017 3rd International Conference on Economics, Social Science, Arts, Education and Management Engineering (ESSAEME 2017)
Series
Advances in Social Science, Education and Humanities Research
Publication Date
July 2017
ISBN
978-94-6252-367-8
ISSN
2352-5398
DOI
10.2991/essaeme-17.2017.414How to use a DOI?
Copyright
© 2017, 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/).

Cite this article

risenwbib
TY  - CONF
AU  - Xinli Wang
AU  - Shijian Liu
AU  - Liping Yan
AU  - Ning Wang
PY  - 2017/07
DA  - 2017/07
TI  - Energy Consumption Forecast Based on Coupling PSO-GPR
BT  - Proceedings of the 2017 3rd International Conference on Economics, Social Science, Arts, Education and Management Engineering (ESSAEME 2017)
PB  - Atlantis Press
SN  - 2352-5398
UR  - https://doi.org/10.2991/essaeme-17.2017.414
DO  - 10.2991/essaeme-17.2017.414
ID  - Wang2017/07
ER  -