Short-term power load forecasting for combined heat and power using CNN-LSTM enhanced by attention mechanism-Reference-Cited by-同舟云学术

Short-term power load forecasting for combined heat and power using CNN-LSTM enhanced by attention mechanism

Published:2023-11 Issue: Volume:282 Page:128274
ISSN:0360-5442
Container-title:Energy
language:en
Short-container-title:Energy

Author:

Wan Anping,Chang Qing,AL-Bukhaiti Khalil^ORCID,He Jiabo

Funder

National Natural Science Foundation of China

China Postdoctoral Science Foundation

Publisher

Elsevier BV

Subject

General Energy,Pollution,Mechanical Engineering,Building and Construction,Electrical and Electronic Engineering,Industrial and Manufacturing Engineering,Civil and Structural Engineering

Reference33 articles.

1. Short-term power load forecasting method based on variational modal decomposition for convolutional long-short-term memory network;L;Modern Electric Power,2022

2. World energy outlook 2019,2019

3. Problems and measures of power grid accommodating large scale wind power;TLYYZX,2010

4. Planning, and operation method of the regional integrated energy system considering economy and environment;Wang;Energy,2019

5. Game theoretical energy management with storage capacity optimization and photo-voltaic cell generated power forecasting in micro grid;Naz;Sustainability,2019

Cited by 75 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A load forecasting approach for integrated energy systems based on aggregation hybrid modal decomposition and combined model;Applied Energy;2024-12

2. A comprehensive review on distributed energy cooperative control and optimization method for energy interconnection system;Electric Power Systems Research;2024-12

3. Estimating the boundary conditions for 3D transient heat conduction by bidirectional long short-term memory network and attention mechanism;International Journal of Heat and Mass Transfer;2024-11

4. A novel hybrid BWO-BiLSTM-ATT framework for accurate offshore wind power prediction;Ocean Engineering;2024-11

5. Short-term power load forecasting based on hybrid feature extraction and parallel BiLSTM network;Computers and Electrical Engineering;2024-11