TY - GEN
T1 - Online Gaussian Process Regression for Short-term Probabilistic Interval Load Prediction
AU - Lu, Di
AU - Wang, Xinghua
AU - Dong, Z. Y.
AU - Peng, Xiangang
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2018/7/2
Y1 - 2018/7/2
N2 - We propose a hybrid probabilistic interval prediction method for short-term load forecasting. The method combines K-means clustering based feature selection approaches and online Gaussian processes regression(OGPR) to generate better prediction results. The K-means clustering algorithm based feature selection are used to select the most relevant features during a dynamical process to better capture the load characters along with time. OGRP, includes dynamically updating the hyper-parameters and training sample sets as two key features, is served as a forecasting engine to carry out load probability interval prediction. The load data from Queensland market, Australia is used to validate the model proposed. The comparative results show that the proposed approach can obtain higher quality prediction interval. © 2018 IEEE.
AB - We propose a hybrid probabilistic interval prediction method for short-term load forecasting. The method combines K-means clustering based feature selection approaches and online Gaussian processes regression(OGPR) to generate better prediction results. The K-means clustering algorithm based feature selection are used to select the most relevant features during a dynamical process to better capture the load characters along with time. OGRP, includes dynamically updating the hyper-parameters and training sample sets as two key features, is served as a forecasting engine to carry out load probability interval prediction. The load data from Queensland market, Australia is used to validate the model proposed. The comparative results show that the proposed approach can obtain higher quality prediction interval. © 2018 IEEE.
KW - K-means clustering based feature selection
KW - Online Gaussian process regression
KW - Probabilistic interval load forecast
UR - https://www.scopus.com/pages/publications/85061744858
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85061744858&origin=recordpage
U2 - 10.1109/POWERCON.2018.8601773
DO - 10.1109/POWERCON.2018.8601773
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9781538664612
T3 - 2018 International Conference on Power System Technology, POWERCON 2018 - Proceedings
SP - 173
EP - 178
BT - 2018 International Conference on Power System Technology, POWERCON 2018 - Proceedings
PB - IEEE
T2 - 2018 International Conference on Power System Technology, POWERCON 2018
Y2 - 6 November 2018 through 9 November 2018
ER -