Short-Term Load Forecasting Based on Deep Learning for End-User Transformer Subject to Volatile Electric Heating Loads

Short-Term Load Forecasting Based on Deep Learning for End-User Transformer Subject to Volatile Electric Heating Loads

Short-Term Load Forecasting (STLF) for End-User Transformer Level (EUTL) is challenging due to the high penetration of Electric Heating Loads (EHLs), which exhibit significant uncertainty, nonlinearity, and variability. In this paper, a STLF model is proposed based on the Stacked Auto-Encoder Extrem...

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Bibliographic Details
Main Authors: Qifang Chen, Mingchao Xia, Teng Lu, Xichen Jiang, Wenxia Liu, Qinfei Sun
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Short-term load forecasting
feature representation
deep learning
stacked auto-encoder
extreme learning machine
Online Access:https://ieeexplore.ieee.org/document/8883177/

Internet

https://ieeexplore.ieee.org/document/8883177/

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