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Temporal Convolutional Network with Adaptive Diffusion Model for Generation–Load Probabilistic Forecasting
DOI:10.3390/en18236179.png)
摘要
En
Keyword:
generation–load forecasting
probabilistic prediction
diffusion model
temporal convolutional network
uncertainty quantification
AI总结
对已上传原文的论文进行重点信息的提取,主要内容包括:简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.2
论文数:
1.6W
被引数:
14.2W
机构
引用论文
Hybrid VMD-CNN-GRU-based model for short-term forecasting of wind power considering spatio-temporal features考虑时空特征的混合vmd-cnn-gru风电功率短期预测模型
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation使用RNN编码器-解码器学习统计机器翻译的短语表示
Probabilistic forecasting of renewable energy and electricity demand using Graph-based Denoising Diffusion Probabilistic Model基于图去噪扩散概率模型的可再生能源和电力需求概率预测
ENERGY AND AI
IF9.6
Variation-based complementarity assessment between wind and solar resources in China基于变异的中国风能与太阳能资源互补性评价
Effective RNN-Based Forecasting Methodology Design for Improving Short-Term Power Load Forecasts: Application to Large-Scale Power-Grid Time Series有效的基于RNN的预测方法设计,以改善短期电力负荷预测: 在大规模电网时间序列中的应用
A Diffusion-Based Probabilistic Ultra-Short-Term Solar Power Prediction Using the Sky Image Sequences基于天空图像序列的基于扩散的概率超短期太阳功率预测
IEEE Access
IF0
DiffLoad: Uncertainty Quantification in Electrical Load Forecasting With the Diffusion ModelDiffLoad:基于扩散模型的电力负荷预测不确定性量化

