MFAMNet: Multi-Scale Feature Attention Mixture Network for Short-Term Load Forecasting
Author:
Affiliation:
1. Power Automation Department, China Electric Power Research Institute, Nanjing 210003, China
2. Jiangsu Key Laboratory of Big Data Analysis Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China
Abstract
Funder
Science and Technology Project of SGCC
Publisher
MDPI AG
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Link
https://www.mdpi.com/2076-3417/13/5/2998/pdf
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3. Sethi, R., and Kleissl, J. (2020, January 23–25). Comparison of Short-Term Load Forecasting Techniques. Proceedings of the 2020 IEEE Conference on Technologies for Sustainability (SusTech), Santa Ana, CA, USA.
4. An adaptive power system management with DG placement and cluster-based load forecasting by CS,K-means and ANN algorithms;Veeresha;Int. J. Power Electron.,2021
5. A relevant data selection method for energy consumption prediction of low energy building based on support vector machine;Paudel;Energy Build.,2017
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