Next Article in Journal
Experimental Investigation of Productivity, Specific Energy Consumption, and Hole Quality in Single-Pulse, Percussion, and Trepanning Drilling of IN 718 Superalloy
Previous Article in Journal
The Influence of Torrefaction Temperature on Hydrophobic Properties of Waste Biomass from Food Processing
Open AccessArticle

A Hybrid Short-Term Load Forecasting Framework with an Attention-Based Encoder–Decoder Network Based on Seasonal and Trend Adjustment

by Zhaorui Meng 1,2 and Xianze Xu 1,*
1
Electronic information school, Wuhan University, Wuhan 430072, China
2
School of computer and information engineering, Xiamen University of Technology, Xiamen 361024, China
*
Author to whom correspondence should be addressed.
Energies 2019, 12(24), 4612; https://doi.org/10.3390/en12244612
Received: 22 October 2019 / Revised: 2 December 2019 / Accepted: 2 December 2019 / Published: 4 December 2019
(This article belongs to the Section State-of-the-Art Energy Related Technologies)
Accurate electrical load forecasting plays an important role in power system operation. An effective load forecasting approach can improve the operation efficiency of a power system. This paper proposes the seasonal and trend adjustment attention encoder–decoder (STA–AED), a hybrid short-term load forecasting approach based on a multi-head attention encoder–decoder module with seasonal and trend adjustment. A seasonal and trend decomposing technique is used to preprocess the original electrical load data. Each decomposed datum is regressed to predict the future electric load value by utilizing the encoder–decoder network with the multi-head attention mechanism. With the multi-head attention mechanism, STA–AED can interpret the prediction results more effectively. A large number of experiments and extensive comparisons have been carried out with a load forecasting dataset from the United States. The proposed hybrid STA–AED model is superior to the other five counterpart models such as random forest, gradient boosting decision tree (GBDT), gated recurrent units (GRUs), Encoder–Decoder, and Encoder–Decoder with multi-head attention. The proposed hybrid model shows the best prediction accuracy in 14 out of 15 zones in terms of both root mean square error (RMSE) and mean absolute percentage error (MAPE). View Full-Text
Keywords: load forecasting; seasonal adjustment; trend adjustment; multi-head attention load forecasting; seasonal adjustment; trend adjustment; multi-head attention
Show Figures

Graphical abstract

MDPI and ACS Style

Meng, Z.; Xu, X. A Hybrid Short-Term Load Forecasting Framework with an Attention-Based Encoder–Decoder Network Based on Seasonal and Trend Adjustment. Energies 2019, 12, 4612.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop