基于对等度量模式的配电网投入产出效益评价与预测

Proceedings of the CSU-EPSA（2020）

Cited 1|Views15

Abstract

针对当前配电网规划方案评价过程中因未能实现投入与产出在对等条件下的衡量而导致评估效果有限、难以支撑精细化投资决策等问题,提出了一种新的配电网投入产出效益评价及预测方法.首先,通过产出效益的支撑条件分析,提出配电网投入与产出效益评估的对等度量模式.在此基础上,选取能够准确反映配电网投入与产出效益对应关系的评价指标,建立了配电网投入产出效益评价指标体系.继而,提出了基于费用效率法的配电网投入产出效益综合评价模型,并结合量化关联关系分析,提出了配电网投入与产出效益预测方法.最后,通过实例分析验证了所提方法的有效性和实用性.

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Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

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