论文标题
无参数的非参数多元概率密度预测的附录
Appendix for Nonparametric Multivariate Probability Density Forecast in Smart Grids With Deep Learning
论文作者
论文摘要
本文提出了基于深度学习的非参数多元密度预测模型。它不仅提供了预测目标中每个随机变量的整个边际分布，而且还揭示了它们之间的未来相关性。与现有的多元密度预测模型不同，提出的方法不需要先验假设预测目标的预测关节概率分布。此外，基于神经网络的通用近似能力，预测靶标的实际关节累积分布函数被所提出的方法中特殊的积极加权深神经网络良好地对待。在全面的评估框架下实施了来自不同方案的数值测试，包括对风速，风能的短期预测以及对总电力负载的日益预测。测试结果证实了所提出的方法比当前多元密度预测模型的优越性，考虑到了现实，预测间隔宽度以及不同随机变量之间的相关性。
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.
