Short-Term Load Forecasting Using EMD-LSTM Neural Networks with a Xgboost Algorithm for Feature Importance Evaluation

By Huiting Zheng, Jiabin Yuan, Long Chen. 2017

623 citations recorded by the source corpus.

Accurate load forecasting is an important issue for the reliable and efficient operation of a power system. This study presents a hybrid algorithm that combines similar days (SD) selection, empirical mode decomposition (EMD), and long short-term memory (LSTM) neural networks to construct a prediction model (i.e., SD-EMD-LSTM) for short-term load forecasting. The extreme gradient boosting-based weighted k-means algorithm is used to evaluate the similarity between the forecasting and historical days. The EMD method is employed to decompose the SD load to several intrinsic mode functions (IMFs) and residual. Separated LSTM neural networks were also employed to forecast each IMF and residual. Lastly, the forecasting values from each LSTM model were reconstructed. Numerical testing demonstrates that the SD-EMD-LSTM method can accurately forecast the electric load.

Subfields: Artificial Intelligence, Control and Systems Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/0765780521cb2c056ca2f10080e8b5ce34ac0d29

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In Artificial Intelligence

1. Abbes Amira
2. Yacine Rezgui
3. Nader Meskin
4. Yassine Himeur
5. Faycal Bensaali

In Electrical and Electronic Engineering

1. Chunhong Zhang
2. Jian Ma
3. Panpan Zhan
4. Chujie Tian

Citations in the corpus

• AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives (2022, methodology): “4 Flowchart of an energy forecasting system based on AI-big data analytics (Zheng et al. 2017)”
• A Deep Neural Network Model for Short-Term Load Forecast Based on Long Short-Term Memory Network and Convolutional Neural Network (2018, methodology): “As a deep learning framework with powerful learning ability to capture the non-stationary and long-term dependencies forecasting horizon [22], recurrent neural networks (RNNs) are effective methods for load forecasting in power grids.”