TY - JOUR
T1 - Clustering-based interval prediction of electric load using multi-objective pathfinder algorithm and Elman neural network
AU - Jiang, Feng
AU - Zhu, Qiannan
AU - Yang, Jiawei
AU - Chen, Guici
AU - Tian, Tianhai
N1 - Funding Information:
This work was supported by the National Natural Science Foundation of China (Grant Nos. 61773401 and 61304067 ), Humanities and Social Science Research Fundation of Ministry of Education of China and Hubei Province Key Laboratory of Systems Science in Metallurgical Process (Wuhan University of Science and Technology) (Grant No. Y202001 ).
Publisher Copyright:
© 2022 Elsevier B.V.
PY - 2022/11
Y1 - 2022/11
N2 - Interval prediction of electric load has aroused widespread concern by the power industry because of variability and uncertainty. To quantify the potential uncertainty associated with prediction, this paper proposes a clustering-based approach to construct prediction intervals (PIs) for electric load data. The singular spectrum analysis (SSA) and k-means clustering are firstly performed to decompose the original data due to the high volatility and nonlinearity of load data. Then, we improve the multi-objective pathfinder algorithm (MOPATH) by using crowding degree of population in order to prevent premature, and further utilize the Elman neural network (ELMAN) optimized by IMOPATH to obtain the subseries PIs of electric load data. In addition, the interval width, coverage probability and deviation are used as three optimization objectives. Finally, the IMOPATH, as an ensemble approach, is applied to ensemble the three PIs together and achieves the final PIs. To verify the performance of the SSA-IMOPATH-ELMAN approach, the proposed approach is compared with 41 models. The forecasting outcomes indicate that PIs of the proposed approach have higher coverage probability, narrower width and lower deviation degree than other benchmark models. Moreover, the proposed approach has good performance on robustness and sensibility.
AB - Interval prediction of electric load has aroused widespread concern by the power industry because of variability and uncertainty. To quantify the potential uncertainty associated with prediction, this paper proposes a clustering-based approach to construct prediction intervals (PIs) for electric load data. The singular spectrum analysis (SSA) and k-means clustering are firstly performed to decompose the original data due to the high volatility and nonlinearity of load data. Then, we improve the multi-objective pathfinder algorithm (MOPATH) by using crowding degree of population in order to prevent premature, and further utilize the Elman neural network (ELMAN) optimized by IMOPATH to obtain the subseries PIs of electric load data. In addition, the interval width, coverage probability and deviation are used as three optimization objectives. Finally, the IMOPATH, as an ensemble approach, is applied to ensemble the three PIs together and achieves the final PIs. To verify the performance of the SSA-IMOPATH-ELMAN approach, the proposed approach is compared with 41 models. The forecasting outcomes indicate that PIs of the proposed approach have higher coverage probability, narrower width and lower deviation degree than other benchmark models. Moreover, the proposed approach has good performance on robustness and sensibility.
KW - Elman neural network
KW - Improved multi-objective pathfinder algorithm
KW - K-means clustering
KW - Prediction intervals
KW - Singular spectrum analysis
UR - http://www.scopus.com/inward/record.url?scp=85137679151&partnerID=8YFLogxK
U2 - 10.1016/j.asoc.2022.109602
DO - 10.1016/j.asoc.2022.109602
M3 - Article
AN - SCOPUS:85137679151
SN - 1568-4946
VL - 129
JO - Applied Soft Computing
JF - Applied Soft Computing
M1 - 109602
ER -