Clustering-based interval prediction of electric load using multi-objective pathfinder algorithm and Elman neural network

Abstract

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.