Short-term load forecasting based on IPSO-DBiLSTM network with variational mode decomposition and attention mechanism

Accurate short-term load forecasting is crucial for the steady operation of the power system and power market schedule planning. The extraction of features and training of prediction models are challenging as the load series is extremely volatile and nonlinear. To address the above issues, we propos...

Full description

Saved in:
Bibliographic Details
Published inApplied intelligence (Dordrecht, Netherlands) Vol. 53; no. 10; pp. 12701 - 12718
Main Authors Huang, Yuan, Huang, Zheng, Yu, JunHao, Dai, XiaoHong, Li, YuanYuan
Format Journal Article
LanguageEnglish
Published New York Springer US 01.05.2023
Springer Nature B.V
Subjects
Accuracy
Artificial Intelligence
Computer Science
Decomposition
Deep learning
Forecasting
Machines
Manufacturing
Mathematical models
Mechanical Engineering
Neural networks
Optimization techniques
Particle swarm optimization
Prediction models
Processes
Residual energy
Attention mechanism
Variational mode decomposition
Short-term load forecasting
Bidirectional long short-term memory
Particle swarm optimization
Online AccessGet full text

Cover

More Information
Summary:Accurate short-term load forecasting is crucial for the steady operation of the power system and power market schedule planning. The extraction of features and training of prediction models are challenging as the load series is extremely volatile and nonlinear. To address the above issues, we propose a deep bidirectional long short-term memory (DBiLSTM) network based on variational mode decomposition (VMD) and an attention mechanism, in which the model hyperparameters are optimized using the improved particle swarm optimization (IPSO) technique. In this study, the mode number k of the VMD is determined by the ratio of residual energy following decomposition. Subsequently, the DBiLSTM is stacked using multiple layers of BiLSTM for a more precise representation of time-series data and the capturing of information at different scales, thereby enabling nonlinear load sequence forecasting and enhancing the accuracy. Finally, the IPSO uses nonlinear decreasing inertia weights to overcome the drawbacks of premature convergence and local optima. The effectiveness and progress of the proposed method are evaluated using the power load dataset from the ninth electrical attribute modeling competition test questions.
ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-022-04174-z