Abstract
Establishing a scientific and reasonable mid- and long-term power load forecasting method is the premise of power industry planning and construction. This paper constructs a hybrid electric load forecasting model based on Gaussian process (GPR) and particle swarm optimization (PSO). The paper uses the PSO algorithm to optimize the parameters in the co-variance function, and uses the modified parameters as the initial value to train the power load in the GPR model. Under the Bayesian framework, the parameters in the co-variance function are again optimized. Finally, the trained GPR model is used to predict the power load, and the results are compared with the auto-regressive integral moving average model and the exponential smoothing model. The verification results show that the hybrid electric load forecasting model based on Gaussian process (GPR) and particle swarm optimization (PSO) has good stability and higher prediction accuracy, and is suitable for medium and long-term electric load forecasting.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Wang, X., Liu, S., Yan, L., Wang, N.: Energy consumption forecast based on coupling PSO-GPR. In: International Conference on Economics, Social Science, Arts, Education and Management Engineering (2017)
Felice, M.D., Alessandri, A., Catalano, F.: Seasonal climate forecasts for medium-term electricity demand forecasting. Appl. Energy 137, 435–444 (2015)
Lee, Y.S., Tong, L.I.: Forecasting energy consumption using a grey model improved by incorporating genetic programming. Energy Convers. Manag. 52(1), 147–152 (2011)
Xu, C., Liu, B.G., Liu, K.Y., Guo, J.Q.: Intelligent analysis model of landslide displacement time series based on coupling PSO-GPR. Rock Soil Mech. 32(6), 1669–1675 (2011)
He, Y., He, Z., Zhang, D.: A study on prediction of customer churn in fixed communication network based on data mining. In: International Conference on Fuzzy Systems and Knowledge Discovery, pp. 92–94 (2009)
Chen, H., Zhou, D.: A forecast of gross energy consumption in china based on GM(1,1) model. Min. Res. Dev. 3, 029 (2007)
Liang, N., Zhang, J.G.: China’s total energy consume forecasting based on grey-RBF neural network. J. Jiamusi Univ. 2, 029 (2008)
Zhang, X., Fan, Y.I.: Boiler soot-blowing optimal control system based on statistical forecast of power load. Power & Energy (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Huang, Y., Hu, J., Cai, Y., Yang, L. (2018). Coupling PSO-GPR Based Medium and Long Term Load Forecasting in Beijing. In: Qiao, J., et al. Bio-inspired Computing: Theories and Applications. BIC-TA 2018. Communications in Computer and Information Science, vol 951. Springer, Singapore. https://doi.org/10.1007/978-981-13-2826-8_43
Download citation
DOI: https://doi.org/10.1007/978-981-13-2826-8_43
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-2825-1
Online ISBN: 978-981-13-2826-8
eBook Packages: Computer ScienceComputer Science (R0)