Imperial College London
Alternate

Professor Goran Strbac

Faculty of Engineering, Department of Electrical and Electronic Engineering

Chair in Electrical Energy Systems
 
 
 
//

Contact

 

+44 (0)20 7594 6169g.strbac

 
 
//

Assistant

 

Miss Guler Eroglu +44 (0)20 7594 6170

 
//

Location

 

1101Electrical EngineeringSouth Kensington Campus

//

Summary

 

Publications

Citation

BibTex format

@article{Sun:2020:10.1109/TPWRS.2019.2924294,
author = {Sun, M and Zhang, T and Wang, Y and Strbac, G and Kang, C},
doi = {10.1109/TPWRS.2019.2924294},
journal = {IEEE Transactions on Power Systems},
pages = {188--201},
title = {Using Bayesian deep learning to capture uncertainty for residential net load forecasting},
url = {http://dx.doi.org/10.1109/TPWRS.2019.2924294},
volume = {35},
year = {2020}
}
Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Decarbonization of electricity systems drives significant and continued investments in distributed energy sources to support the cost-effective transition to low-carbon energy systems. However, the rapid integration of distributed photovoltaic (PV) generation presents great challenges in obtaining reliable and secure grid operations because of its limited visibility and intermittent nature. Under this reality, net load forecasting is facing unprecedented difficulty in answering the following question: how can we accurately predict the net load while capturing the massive uncertainties arising from distributed PV generation and load, especially in the context of high PV penetration? This paper proposes a novel probabilistic day-ahead net load forecasting method to capture both epistemic uncertainty and aleatoric uncertainty using Bayesian deep learning, which is a new field that combines Bayesian probability theory and deep learning. The proposed methodological framework employs clustering in subprofiles and considers residential rooftop PV outputs as input features to enhance the performance of aggregated net load forecasting. Numerical experiments have been carried out based on fine-grained smart meter data from the Australian grid with separately recorded measurements of rooftop PV generation and loads. The results demonstrate the superior performance of the proposed scheme compared with a series of state-of-theart methods and indicate the importance and effectiveness of subprofile clustering and high PV visibility.
AU  - Sun,M
AU  - Zhang,T
AU  - Wang,Y
AU  - Strbac,G
AU  - Kang,C
DO  - 10.1109/TPWRS.2019.2924294
EP  - 201
PY  - 2020///
SN  - 0885-8950
SP  - 188
TI  - Using Bayesian deep learning to capture uncertainty for residential net load forecasting
T2  - IEEE Transactions on Power Systems
UR  - http://dx.doi.org/10.1109/TPWRS.2019.2924294
UR  - http://hdl.handle.net/10044/1/71620
VL  - 35
ER  -
Download