.png){kind=icon}
返回
Nonparametric Multivariate Probability Density Forecast in Smart Grids With Deep Learning
.png){kind=icon}
PRE
AI
DOI：10.1109/TPWRS.2022.3218306.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper proposes a nonparametric multivariate density forecast model based on deep learning. It not only offers the whole marginal distribution of each random variable in forecasting targets, but also reveals the future correlation between them. Differing from existing multivariate density forecast models, the proposed method requires no a priori hypotheses on the forecasted joint probability distribution of forecasting targets. In addition, based on the universal approximation capability of neural networks, the real joint cumulative distribution functions of forecasting targets are well-approximated by a special positive-weighted deep neural network in the proposed method. Numerical tests from different scenarios were implemented under a comprehensive verification framework for evaluation, including the very short-term forecast of the wind speed, wind power, and the day-ahead forecast of the aggregated electricity load. Testing results corroborate the superiority of the proposed method over current multivariate density forecast models considering the accordance with reality, prediction interval width, and correlations between different random variables
Keyword:
Deep learning
multivariate monotone neural network (NN)
NNs
multivariate probability density forecast
期刊
IF:
7.2
论文数:
1.1W
被引数:
5.0W
机构
引用论文
Multivariate time series forecasting via attention-based encoder-decoder framework.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏Non-parametric probabilistic forecasts of wind power: Required properties and evaluation风力发电的非参数概率预测: 要求的属性和评估WIND ENERGY
WIND ENERGYIF3.3
PREAI
分享收藏
An Adaptive Ensemble Data Driven Approach for Nonparametric Probabilistic Forecasting of Electricity Load一种自适应集成数据驱动的电力负荷非参数概率预测方法
OAAI
OAAI分享收藏
查看更多内容