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Rough Set Theory With Discriminant Analysis In Analyzing Electricity Loads

机译：粗糙集理论与判别分析在电力负荷分析中的应用

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摘要

With the ability to deal with both numeric and nominal information, rough set theory (RST), which can express knowledge in a rule-based form, has been one of the most important techniques in data analysis. However, applications of rough set theory for analyzing electricity loads are not widely discussed. Thus, this investigation employs rough set theory to analyze electricity loads. Additionally, to reduce the time generating reducts by rough set theory, linear discriminant analysis (LDA) is used to generate a reduct for rough set model. Therefore, this study designs a hybrid discriminant analysis and rough set model (DARST) to provide decision rules representing relations in an electric load information system. In this investigation, nine condition factors and variations of electricity loads are employed to examine the feasibility of the hybrid model. Experimental results reveal that the proposed model can efficiently and accurately analyze the relation between condition variables and variations of electricity loads. Consequently, the proposed model is a promising alternative for developing an electric load information system and offers decision rules base for the utility management as well as operations staff.
机译：具有处理数字和名义信息的能力，可以以基于规则的形式表达知识的粗糙集理论（RST）已成为数据分析中最重要的技术之一。然而，粗糙集理论在分析电力负荷中的应用并未得到广泛讨论。因此，本研究采用粗糙集理论来分析电力负荷。另外，为了减少粗糙集理论生成还原的时间，线性判别分析（LDA）用于生成粗糙集模型的还原。因此，本研究设计了一种混合判别分析和粗糙集模型（DARST），以提供表示电力负荷信息系统中关系的决策规则。在这项调查中，九个条件因素和电力负荷的变化被用来检验混合模型的可行性。实验结果表明，该模型能够有效，准确地分析条件变量与电力负荷变化之间的关系。因此，该模型是开发电力负荷信息系统的有希望的替代方案，并为公用事业管理人员和运营人员提供了决策规则库。

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