Imperial College London
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DrMingyangSun

Faculty of Engineering, Department of Electrical and Electronic Engineering

Honorary Lecturer
 
 
 
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Contact

 

mingyang.sun11

 
 
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Location

 

Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Wang:2018:10.1109/TSG.2018.2807985,
author = {Wang, Y and Qixin, C and Sun, M and Kang, C and Qing, X},
doi = {10.1109/TSG.2018.2807985},
journal = {IEEE Transactions on Smart Grid},
title = {An ensemble forecasting method for the aggregated load with sub profiles},
url = {http://dx.doi.org/10.1109/TSG.2018.2807985},
volume = {9},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - With the prevalence of smart meters, fine-grained subprofiles reveal more information about the aggregated load and further help improve the forecasting accuracy. Ensemble is an effective approach for load forecasting. It either generates multiple training datasets or applies multiple forecasting models to produce multiple forecasts. In this letter, a novel ensemble method is proposed to forecast the aggregated load with subprofiles where the multiple forecasts are produced by different groupings of subprofiles. Specifically, the subprofiles are first clustered into different groups and forecasting is conducted on the grouped load profiles individually. Thus, these forecasts can be summed to form the aggregated load forecast. In this way, different aggregated load forecasts can be obtained by varying the number of clusters. Finally, an optimal weighted ensemble approach is employed to combine these forecasts and provide the final forecasting result. Case studies are conducted on two open datasets and verify the effectiveness and superiority of the proposed method.
AU  - Wang,Y
AU  - Qixin,C
AU  - Sun,M
AU  - Kang,C
AU  - Qing,X
DO  - 10.1109/TSG.2018.2807985
PY  - 2018///
SN  - 1949-3061
TI  - An ensemble forecasting method for the aggregated load with sub profiles
T2  - IEEE Transactions on Smart Grid
UR  - http://dx.doi.org/10.1109/TSG.2018.2807985
UR  - http://hdl.handle.net/10044/1/56889
VL  - 9
ER  -
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