Decomposition strategy and attention-based long short-term memory network for multi-step ultra-short-term agricultural power load forecasting[Formula presented]

Feifei Yang, Xueqian Fu, Qiang Yang, Zheng Chu

Research output: Journal PublicationArticlepeer-review

2 Citations (Scopus)

Abstract

Accurate forecasting of the agricultural power load is important for rural electricity networks’ safe and stable operation. But it is more uncertain and difficult to forecast than industrial, commercial, and residential loads. Multivariate time series prediction methods have problems such as poor refinement of weather services and difficulty in quantifying the influencing factors. Univariate time series prediction methods should be emphasized, and the characteristic information in the historical load data should be fully exploited. Therefore, this paper systematically investigates the decomposition strategy and attention-based recurrent neural network for univariate time-series multi-step prediction. The decomposition strategy is employed to generate derived variable sequences from the target sequence as inputs for the model, while the dual-stage attention mechanism is used for dynamically selecting features. A one-year real-world dataset of agricultural power loads in southern China is used for validation. The effectiveness of two different decomposition algorithms on dual-stage attention-based recurrent neural network (DARNN) is discussed, then the proposed ensemble empirical mode decomposition and dual-stage attention-based recurrent neural network (EEMD-DARNN) is compared with four univariate time series prediction models and three ablation models. The proposed model is optimal in four evaluation indicators, with R2, RMSE, MAPE, and MAE of 0.970, 12.298, 0.040, and 7.335 respectively in the shortest forecast horizon (15 min), and 0.941, 17.305, 0.065 and 11.347 respectively in the longest forecast horizon (1 h). It is a significant improvement over the existing models. In conclusion, EEMD-DARNN can achieve more accurate ultra-short-term agricultural power load forecasting.

Original languageEnglish
Article number122226
JournalExpert Systems with Applications
Volume238
DOIs
Publication statusPublished - 15 Mar 2024

Keywords

  • Agricultural power load forecasting
  • Attention mechanism
  • Decomposition strategy
  • Recurrent neural network
  • Univariate time series prediction

ASJC Scopus subject areas

  • General Engineering
  • Computer Science Applications
  • Artificial Intelligence

Fingerprint

Dive into the research topics of 'Decomposition strategy and attention-based long short-term memory network for multi-step ultra-short-term agricultural power load forecasting[Formula presented]'. Together they form a unique fingerprint.

Cite this