期刊
APPLIED ENERGY
卷 309, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.apenergy.2021.118341
关键词
Neural networks; Bayesian deep learning; Mixture density; Probabilistic forecasting; Electric load
This work presents a novel approach to improve the trustworthiness of neural network based load forecasting systems by integrating predictive distributions and uncertainty sources. Experimental results demonstrate significant performance improvements in load forecasting.
This work presents a novel approach to address a challenging and still unsolved problem of neural network based load forecasting systems, that despite the significant results reached in terms of prediction error reduction, still lack suitable indications regarding sample-wise trustworthiness of their predictions. The present approach is framed on Bayesian Mixture Density Networks, enhancing the mapping capabilities of neural networks by integrated predictive distributions, and encompassing both aleatoric and epistemic uncertainty sources. An end-to-end training method is developed, aimed to discover the latent functional relation to conditioning variables, characterize the inherent load stochasticity, and convey parameters uncertainty in a unique framework. To achieve reliable and computationally scalable estimators, both Mean Field variational inference and deep ensembles are integrated. Experiments have been performed on short-term load forecasting tasks at both regional and fine-grained household scale, to investigate heterogeneous operating conditions. Different architectural configurations are compared, showing by Continuous Ranked Probability Score based tests that significant performance improvements are achieved by integrating flexible aleatoric uncertainty patterns and multi-modalities in the parameters posterior space.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据