Mid- to Long-Term Electric Load Forecasting Based on the EMD–Isomap–Adaboost Model

Xuguang Han, Jingming Su, Yan Hong, Pingshun Gong and Danping Zhu
Additional contact information
Xuguang Han: School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China
Jingming Su: School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China
Yan Hong: School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China
Pingshun Gong: School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China
Danping Zhu: School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China

Sustainability, 2022, vol. 14, issue 13, 1-15

Abstract: Accurate load forecasting is an important issue for the reliable and efficient operation of a power system. In this study, a hybrid algorithm (EMDIA) that combines empirical mode decomposition (EMD), isometric mapping (Isomap), and Adaboost to construct a prediction mode for mid- to long-term load forecasting is developed. Based on full consideration of the meteorological and economic factors affecting the power load trend, the EMD method is used to decompose the load and its influencing factors into multiple intrinsic mode functions (IMF) and residuals. Through correlation analysis, the power load is divided into fluctuation term and trend term. Then, the key influencing factors of feature sequences are extracted by Isomap to eliminate the correlations and redundancy of the original multidimensional sequences and reduce the dimension of model input. Eventually, the Adaboost prediction method is adopted to realize the prediction of the electrical load. In comparison with the RF, LSTM, GRU, BP, and single Adaboost method, the prediction obtained by this proposed model has higher accuracy in the mean absolute percentage error (MAPE), mean absolute error (MAE), root mean square error (RMSE), and determination coefficient (R 2 ). Compared with the single Adaboost algorithm, the EMDIA reduces MAE by 11.58, MAPE by 0.13%, and RMSE by 49.93 and increases R 2 by 0.04.

Keywords: power load forecasting; Isomap; Adaboost; differential empirical mode decomposition (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/14/13/7608/pdf (application/pdf)
https://www.mdpi.com/2071-1050/14/13/7608/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:14:y:2022:i:13:p:7608-:d:845063

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().