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

@inproceedings{Zhang:2018,
author = {Zhang, J and Wang, Y and Sun, M and Zhang, N and Kang, C},
publisher = {IEEE},
title = {Constructing probabilistic load forecast from multiple point forecasts: a bootstrap based approach},
url = {http://hdl.handle.net/10044/1/57661},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - Probabilistic  load  forecast  presents  more  informa-tion on the possible deviation of forecast than the point forecast.There   are   sufficient   regression   models   that   can   make   pointforecasts.  An  intuitive  question  can  be  raised:Is  there  a  wayto combine the point forecasts to construct a probability or intervalforecast?In this  paper, a bootstrap  based ensemble approach  isput  forward  to  construct  forecast  intervals  from  multiple  pointforecasts. Specifically, multiple point forecasting models are firsttrained  based  on  the  bootstrap  sampled  training  datasets  anddifferent forecasting models. Then, bootstrap is applied again tothe  multiple  point  forecasts.  Finally,  the  quantiles  are  estimatedaccording  to  the  distribution  of  the  sampled  point  forecasts.Two  common  machine  learning  methods,  random  forest  (RF)and  gradient  boosting  regression  tree  (GBRT),  are  combinedto  test  the  feasibility  of  the  proposed  forecasting  framework.Compared  with  quantile  RF  (Q-RF)  and  quantile  GBRT  (Q-GBRT),  numerical  experiments  demonstrate  its  advantage  overQ-RF  and  Q-GBRT.
AU  - Zhang,J
AU  - Wang,Y
AU  - Sun,M
AU  - Zhang,N
AU  - Kang,C
PB  - IEEE
PY  - 2018///
TI  - Constructing probabilistic load forecast from multiple point forecasts: a bootstrap based approach
UR  - http://hdl.handle.net/10044/1/57661
ER  -
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