Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Application of ALO-ELM in Load Forecasting Based on Big Data

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. Preprints 2021, 2021100302 (doi: 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 (doi: 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.

Keywords

load forecasting; extreme learning machine (ELM); ant lion optimization (ALO) ;parameter optimization; model.

Subject

ENGINEERING, Electrical & Electronic Engineering

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