Developing a wind power forecasting system based on deep learning with attention mechanism-Reference-Cited by-同舟云学术

Developing a wind power forecasting system based on deep learning with attention mechanism

Published:2022-10 Issue: Volume:257 Page:124750
ISSN:0360-5442
Container-title:Energy
language:en
Short-container-title:Energy

Author:

Tian Chaonan,Niu Tong,Wei Wei

Funder

Jinan Office of Philosophy and Social Science

National Natural Science Foundation of China

China Postdoctoral Science Foundation

Natural Science Foundation of Nantong City

Publisher

Elsevier BV

Subject

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

Reference43 articles.

1. Comparison of the goodness-of-fit of intelligent-optimized wind speed distributions and calculation in high-altitude wind-energy potential assessment;Li;Energy Convers Manag,2021

2. A review of wind speed and wind power forecasting with deep neural networks;Wang;Appl Energy,2021

3. Forecasting intra-hour variance of photovoltaic power using a new integrated model;Guermoui;Energy Convers Manag,2021

4. Ensemble forecasting system for short-term wind speed forecasting based on optimal sub-model selection and multi-objective version of mayfly optimization algorithm;Liu;Expert Syst Appl,2021

5. Two novel hybrid linear and nonlinear models for wind speed forecasting;Huang;Energy Convers Manag,2021

Cited by 50 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. Self-attention mechanism to enhance the generalizability of data-driven time-series prediction: A case study of intra-hour power forecasting of urban distributed photovoltaic systems;Applied Energy;2024-11

2. Improving short-term wind power forecasting in Senegal’s flagship wind farm: a deep learning approach with attention mechanism;Electrical Engineering;2024-09-09

3. Optimizing multi-step wind power forecasting: Integrating advanced deep neural networks with stacking-based probabilistic learning;Applied Energy;2024-09

4. Creating synthetic energy meter data using conditional diffusion and building metadata;Energy and Buildings;2024-06

5. Novel wind power ensemble forecasting system based on mixed-frequency modeling and interpretable base model selection strategy;Energy;2024-06