Multi-step-ahead electricity load forecasting using a novel hybrid architecture with decomposition-based error correction strategy

Deyun Wang, Chenqiang Yue and Adnen ElAmraoui

Chaos, Solitons & Fractals, 2021, vol. 152, issue C

Abstract: In this study, a novel architecture combining a hybrid learning paradigm and an error correction strategy is presented for multi-step-ahead electricity load forecasting. The detail of the proposed architecture is provided as follows: (1) a novel hybrid learning paradigm based on complementary ensemble empirical mode decomposition (CEEMD) and backpropagation (BP) neural network improved by particle swarm optimization (PSO-BP) is developed for preliminary prediction of the electricity load; (2) an error prediction approach based on variational mode decomposition (VMD) and PSO-BP is established for prediction of the subsequent error; (3) calibrate the preliminary prediction values using the forecast results of the error prediction model. Specifically, in the error correction process, the original data series is separated into three subsets to generate a reasonable historical error series used for establishing the error prediction model. Two case studies based on the data of PJM and Ontario electricity markets are presented and investigated to assess the effectiveness of the proposed architecture. The evaluation results demonstrate that the proposed architecture can yield results in higher accuracy than other benchmark models considered in this study.

Keywords: Electricity load; Multi-step-ahead forecasting; Error correction strategy; Time series decomposition; Hybrid forecasting model (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960077921008079
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:chsofr:v:152:y:2021:i:c:s0960077921008079

DOI: 10.1016/j.chaos.2021.111453

Access Statistics for this article

Chaos, Solitons & Fractals is currently edited by Stefano Boccaletti and Stelios Bekiros

More articles in Chaos, Solitons & Fractals from Elsevier
Bibliographic data for series maintained by Thayer, Thomas R. ().