Version 1
: Received: 19 October 2021 / Approved: 21 October 2021 / Online: 21 October 2021 (09:34:56 CEST)
How to cite:
He, M.; Li, Y.; Zou, W.; Duan, X. Application of ALO-ELM in Load Forecasting Based on Big Data. Preprints2021, 2021100302. https://doi.org/10.20944/preprints202110.0302.v1
He, M.; Li, Y.; Zou, W.; Duan, X. Application of ALO-ELM in Load Forecasting Based on Big Data. Preprints 2021, 2021100302. https://doi.org/10.20944/preprints202110.0302.v1
He, M.; Li, Y.; Zou, W.; Duan, X. Application of ALO-ELM in Load Forecasting Based on Big Data. Preprints2021, 2021100302. https://doi.org/10.20944/preprints202110.0302.v1
APA Style
He, M., Li, Y., Zou, W., & Duan, X. (2021). Application of ALO-ELM in Load Forecasting Based on Big Data. Preprints. https://doi.org/10.20944/preprints202110.0302.v1
Chicago/Turabian Style
He, M., Wan Zou and Xiangxi Duan. 2021 "Application of ALO-ELM in Load Forecasting Based on Big Data" Preprints. https://doi.org/10.20944/preprints202110.0302.v1
Abstract
The load of power system changes with the development of economy, short-term load forecasting play a very important role in dispatching and management of power system. In this paper, the Ant Lion Optimizer (ALO) is introduced to improve the input weights and hidden-layer Matrix of extreme learning machine (ELM), after the parameters of ELM are optimized by ALO, then input nodes, hidden layer nodes and output nodes are determined, so a load forecasting model based on ALO-ELM combined algorithm is established. The proposed method is illustrated based on the historical load data of a city in China. The results show that the average absolute error of short-term load demand predicted by ALO-ELM model is 1.41, while that predicted by ELM is 4.34, the proposed ALO-ELM algorithm is superior to the ELM and meet the requirements of engineering accuracy, which proves the effectiveness of proposed method.
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
