Short-Term Load Forecasting Based on Deep Learning Bidirectional LSTM Neural Network
Accurate load forecasting guarantees the stable and economic operation of power systems. With the increasing integration of distributed generations and electrical vehicles, the variability and randomness characteristics of individual loads and the distributed generation has increased the complexity...
Main Authors: | Changchun Cai, Yuan Tao, Tianqi Zhu, Zhixiang Deng |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-09-01
|
Series: | Applied Sciences |
Subjects: | |
Online Access: | https://www.mdpi.com/2076-3417/11/17/8129 |
Similar Items
-
Deep Learning-Assisted Short-Term Load Forecasting for Sustainable Management of Energy in Microgrid
by: Arash Moradzadeh, et al.
Published: (2021-02-01) -
An Extensible Framework for Short-Term Holiday Load Forecasting Combining Dynamic Time Warping and LSTM Network
by: Jeffrey Gunawan, et al.
Published: (2021-01-01) -
Ultra-Short-Term Load Demand Forecast Model Framework Based on Deep Learning
by: Hongze Li, et al.
Published: (2020-09-01) -
Multiple-Load Forecasting for Integrated Energy System Based on Copula-DBiLSTM
by: Jieyun Zheng, et al.
Published: (2021-04-01) -
Deep Ensemble Learning Model for Short-Term Load Forecasting within Active Learning Framework
by: Zengping Wang, et al.
Published: (2019-10-01)