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DC Field | Value | Language |
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dc.contributor.author | Lai, CS | - |
dc.contributor.author | Yang, Y | - |
dc.contributor.author | Pan, K | - |
dc.contributor.author | Zhang, J | - |
dc.contributor.author | H.L. Yuan, HL | - |
dc.contributor.author | Ng, WWY | - |
dc.contributor.author | Gao, Y | - |
dc.contributor.author | Zhao, Z | - |
dc.contributor.author | Wang, T | - |
dc.contributor.author | Shahidehpour, M | - |
dc.contributor.author | Lai, LL | - |
dc.date.accessioned | 2020-12-14T15:48:31Z | - |
dc.date.available | 2020-12-14T15:48:31Z | - |
dc.date.issued | 2020-12-03 | - |
dc.identifier.citation | Lai, C.S. et al. (2021) 'Multi-View Neural Network Ensemble for Short and Mid-Term Load Forecasting ', IEEE Transactions on Power Systems, 36 (4), pp. 2992 - 3003. doi: 10.1109/tpwrs.2020.3042389. | en_US |
dc.identifier.issn | 0885-8950 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/21994 | - |
dc.description.abstract | Accurate load forecasting is essential to the operation and planning of power systems and electricity markets. In this paper, an ensemble of radial basis function neural networks (RBFNNs) is proposed which is trained by minimizing the localized generalization error for short-term and mid-term load forecasting. Exogenous features and features extracted from load series (with long short-term memory networks and multi-resolution wavelet transform) in various timescales are used to train the ensemble of RBFNNs. Multiple RBFNNs are fused as an ensemble model with high generalization capability using a proposed weighted fusion method based on the localized generalization error model. Experimental results on three practical datasets show that compared with other forecasting methods, the proposed method reduces the mean absolute percentage error (MAPE), mean squared error (MSE), mean absolute error (MAE) by at least 0.12%, 8.46 (MW)2, 0.83 MW in mid-term load forecasting (i.e., to predict the daily peak load of next month), respectively, and reduces the MAPE, MSE by at least 0.19%, 2009.69 (MW)2 and 0.30%, 3697.18 (MW)2 in half-hour-ahead forecasting and day-ahead forecasting, respectively. | en_US |
dc.description.sponsorship | 10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 61876066, 51907031, 61903091 and 61572201); 10.13039/501100010256-Guangzhou Municipal Science and Technology Project (Grant Number: 201804010245); 10.13039/501100010226-Department of Education of Guangdong Province; 10.13039/501100010226-Department of Education of Guangdong Province (Grant Number: 2016KCXTD022); Brunel Research Initiative and Enterprise Fund, U.K. | en_US |
dc.format.extent | 2992 - 3003 | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
dc.rights | Copyright © 2020 Institute of Electrical and Electronics Engineers (IEEE). Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works by sending a request to pubs-permissions@ieee.org. See: https://www.ieee.org/publications/rights/rights-policies.html | - |
dc.rights.uri | https://www.ieee.org/publications/rights/rights-policies.html | - |
dc.subject | multi-view | en_US |
dc.subject | ensemble | en_US |
dc.subject | long short-term memory network | en_US |
dc.subject | load forecasting | en_US |
dc.title | Multi-View Neural Network Ensemble for Short and Mid-Term Load Forecasting | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.1109/tpwrs.2020.3042389 | - |
dc.relation.isPartOf | IEEE Transactions on Power Systems | - |
pubs.publication-status | Published | - |
dc.identifier.eissn | 1558-0679 | - |
dc.rights.holder | Institute of Electrical and Electronics Engineers (IEEE) | - |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
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