Imperial College London
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DrFeiTeng

Faculty of Engineering, Department of Electrical and Electronic Engineering

Senior Lecturer
 
 
 
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Contact

 

+44 (0)20 7594 6178f.teng Website CV

 
 
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Location

 

1116Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Sun:2019:10.1109/TSG.2019.2895333,
author = {Sun, M and Wang, Y and Teng, F and Ye, Y and Strbac, G and Kang, C},
doi = {10.1109/TSG.2019.2895333},
journal = {IEEE Transactions on Smart Grid},
pages = {6014--6028},
title = {Clustering-based residential baseline estimation: a probabilistic perspective},
url = {http://dx.doi.org/10.1109/TSG.2019.2895333},
volume = {10},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Demand Response (DR) is one of the most cost-effective solutions for providing flexibility to power systems. The extensive deployment of DR trials and the roll-out of smart meters enable the quantification of consumer responsiveness to price signals via baseline estimation. The traditional deterministic baseline estimation approach can provide only a single value without consideration of uncertainty. This paper proposes a novel probabilistic baseline estimation framework that consists of a daily load profile pool construction stage, a deep learning-based clustering stage, an optimal cluster selection stage, and a quantile regression forests model construction stage. In particular, the concept of a daily load profile pool is introduced, and a deep-learning-based clustering approach is employed to handle a large number of daily patterns to further improve the baseline estimation performance. Case studies have been conducted on fine-grained smart meter data collected from a real dynamic time-of-use (dTOU) tariffs trial of the Low Carbon London (LCL) project. The superior performance of the proposed method is demonstrated based on a series of evaluation metrics regarding both deterministic and probabilistic estimation results.
AU  - Sun,M
AU  - Wang,Y
AU  - Teng,F
AU  - Ye,Y
AU  - Strbac,G
AU  - Kang,C
DO  - 10.1109/TSG.2019.2895333
EP  - 6028
PY  - 2019///
SN  - 1949-3061
SP  - 6014
TI  - Clustering-based residential baseline estimation: a probabilistic perspective
T2  - IEEE Transactions on Smart Grid
UR  - http://dx.doi.org/10.1109/TSG.2019.2895333
UR  - http://hdl.handle.net/10044/1/67214
VL  - 10
ER  -
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