TY - GEN
T1 - Probabilistic Load Prediction with Risk-Severity Score
AU - Aprillia, Happy
AU - Yang, Hong Tzer
AU - Huang, Chao Ming
N1 - Funding Information:
ACKNOWLEDGMENT This work was supported by the Ministry of Science and Technology, Taiwan, under Grant MOST 109-3116-F-006-017-CC2 and 108-3116-F-168-001-CC2.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/10/18
Y1 - 2020/10/18
N2 - Emerging interest in probabilistic load forecasting (PLF) has increased because it can address load uncertainty better than point forecasting. This paper proposed a new strategy to treat the input of PLF as a probability map, which enables an actual pictorial to depict the prediction outcome risk of a load demand profile according to the preferred length of the prediction horizon. The proposed probabilistic method consists of optimal signal decomposition, prediction model construction and an evaluation strategy to obtain the prediction interval risk outcome. Whale optimization algorithm-discrete wavelet transformation (WOA-DWT) is employed to detect optimal input decomposition. The quantile regression random forest (QRRF) is utilized to build a prediction interval from various quantiles. A novel scheme to calculate the prediction outcome risk due to current input, which provides a degree of confidence, is also presented. The efficacy of prediction interval is verified using the Independent System Operator - New England (ISO-NE) set with the Winkler score and proposed risk-severity score.
AB - Emerging interest in probabilistic load forecasting (PLF) has increased because it can address load uncertainty better than point forecasting. This paper proposed a new strategy to treat the input of PLF as a probability map, which enables an actual pictorial to depict the prediction outcome risk of a load demand profile according to the preferred length of the prediction horizon. The proposed probabilistic method consists of optimal signal decomposition, prediction model construction and an evaluation strategy to obtain the prediction interval risk outcome. Whale optimization algorithm-discrete wavelet transformation (WOA-DWT) is employed to detect optimal input decomposition. The quantile regression random forest (QRRF) is utilized to build a prediction interval from various quantiles. A novel scheme to calculate the prediction outcome risk due to current input, which provides a degree of confidence, is also presented. The efficacy of prediction interval is verified using the Independent System Operator - New England (ISO-NE) set with the Winkler score and proposed risk-severity score.
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U2 - 10.1109/IECON43393.2020.9255342
DO - 10.1109/IECON43393.2020.9255342
M3 - Conference contribution
AN - SCOPUS:85097785437
T3 - IECON Proceedings (Industrial Electronics Conference)
SP - 1692
EP - 1697
BT - Proceedings - IECON 2020
PB - IEEE Computer Society
T2 - 46th Annual Conference of the IEEE Industrial Electronics Society, IECON 2020
Y2 - 19 October 2020 through 21 October 2020
ER -