A Short-Term Load Forecasting Model of LSTM Neural Network considering Demand Response
As one of the key technologies for accelerating the construction of the ubiquitous Internet of Things, demand response (DR) not only guides users to participate in power market operations but also increases the randomness of grid operations and the difficulty of load forecasting. In order to solve t...
Saved in:
Main Authors: | Xifeng Guo, Qiannan Zhao, Shoujin Wang, Dan Shan, Wei Gong |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2021-01-01
|
Series: | Complexity |
Online Access: | http://dx.doi.org/10.1155/2021/5571539 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Multifeature Short-Term Power Load Forecasting Based on GCN-LSTM
by: Houhe Chen, et al.
Published: (2023-01-01) -
An optimized method for short-term load forecasting based on feature fusion and ConvLSTM-3D neural network
by: Xiaofeng Yang, et al.
Published: (2025-01-01) -
Deep Learning-Assisted Short-Term Power Load Forecasting Using Deep Convolutional LSTM and Stacked GRU
by: Fath U Min Ullah, et al.
Published: (2022-01-01) -
Short-Term Passenger Flow Forecasting for Rail Transit considering Chaos Theory and Improved EMD-PSO-LSTM-Combined Optimization
by: Lixin Zhao, et al.
Published: (2023-01-01) -
A New Strategy for Short-Term Load Forecasting
by: Yi Yang, et al.
Published: (2013-01-01)