A novel ensemble electricity load forecasting system based on a decomposition-selection-optimization strategy

Ying Wang, Hongmin Li, Atif Jahanger, Qiwei Li, Biao Wang and Daniel Balsalobre-Lorente

Energy, 2024, vol. 312, issue C

Abstract: Electricity load forecasting exhibits an irreplaceable role in enhancing the dispatching and management efficiency of power systems. However, the majority of existing research neglected the feature extraction of original series as well as interval prediction, which leads to inevitable forecasting bias and insufficient information. To fill the gaps, a novel ensemble system is proposed to realize both point and interval multi-step forecasting results. Specifically, the original series is divided and reconstructed into multi-scale sub-series by the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and the adaptive Lempel-Ziv complexity (ALZC) algorithms, while the input variables are determined by a two-stage feature selection method. Then a sub-predictor selection strategy is employed to select the effective forecasting model for each sub-series, and the improved multi-objective salp swarm algorithm (IMSSA) is utilized to optimize the ensemble point predictions. Further, the point error-based interval forecasting fitted by the forecasted fluctuation properties is conducted for uncertainty analysis. To testify the efficiency of the system, three 30-min electricity load datasets from Australia are employed for experiments. Based on the empirical results, the average mean absolute percentage errors of one-step, two-step, and three-step point predictions on three datasets are 0.2621 %, 0.4512 %, and 0.6305 %, respectively, and the average interval coverage probabilities are 0.9867, 0.9900, and 0.9767 for three datasets of one-step, two-step, and three-step interval predictions under the significance level of 95 %. The results indicated that the system has superior forecasting ability and can supply scientific and comprehensive references for power systems.

Keywords: Electricity load; Multiscale decomposition; Feature extraction; Sub-predictor selection; Improved multi-objective optimization; Ensemble system (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544224033000
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:312:y:2024:i:c:s0360544224033000

DOI: 10.1016/j.energy.2024.133524

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().