A Weighted Evolving Fuzzy Neural Network for Electricity Demand Forecasting

2009 First Asian Conference on Intelligent Information and Database Systems Pub Date : 2009-04-01 DOI:10.1109/ACIIDS.2009.93

P. Chang, C. Fan, J. Hsieh

{"title":"A Weighted Evolving Fuzzy Neural Network for Electricity Demand Forecasting","authors":"P. Chang, C. Fan, J. Hsieh","doi":"10.1109/ACIIDS.2009.93","DOIUrl":null,"url":null,"abstract":"This research develops a weighted evolving fuzzy neural network for electricity demand forecasting in Taiwan. This study modifies the Evolving Fuzzy Neural Network Framework (EFuNN framework) and adopts a weighted factor to calculate the importance of each factor among these different rules. In addition, an exponential transfer function (exp(-D)) is employed to transfer the distance of any two factors into the value of similarity among different rules, thus a different rule clustering method is developed accordingly. Seven explanatory factors identified by the Taiwan Power Company will affect the power consumption in Taiwan and these seven factors will be inputted into the WEFuNN to forecast the electricity demand in the future. The historical data will be applied to train the WEFuNN and then forecasts the future electricity demands. Finally, the model is compared with other approaches proposed in the literature. The experimental results reveal that the MAPE for WEFuNN model is 6.11% which outperforms the others. In summary, the WEFuNN model can be applied practically as an electricity demand forecasted tool in Taiwan.","PeriodicalId":275776,"journal":{"name":"2009 First Asian Conference on Intelligent Information and Database Systems","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 First Asian Conference on Intelligent Information and Database Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ACIIDS.2009.93","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 12

Abstract

This research develops a weighted evolving fuzzy neural network for electricity demand forecasting in Taiwan. This study modifies the Evolving Fuzzy Neural Network Framework (EFuNN framework) and adopts a weighted factor to calculate the importance of each factor among these different rules. In addition, an exponential transfer function (exp(-D)) is employed to transfer the distance of any two factors into the value of similarity among different rules, thus a different rule clustering method is developed accordingly. Seven explanatory factors identified by the Taiwan Power Company will affect the power consumption in Taiwan and these seven factors will be inputted into the WEFuNN to forecast the electricity demand in the future. The historical data will be applied to train the WEFuNN and then forecasts the future electricity demands. Finally, the model is compared with other approaches proposed in the literature. The experimental results reveal that the MAPE for WEFuNN model is 6.11% which outperforms the others. In summary, the WEFuNN model can be applied practically as an electricity demand forecasted tool in Taiwan.

查看原文本刊更多论文

电力需求预测的加权演化模糊神经网络

本研究提出一种加权演化模糊神经网路，用于台湾地区电力需求预测。本文对进化模糊神经网络框架(EFuNN框架)进行了改进，采用加权因子来计算这些不同规则中每个因子的重要性。此外，利用指数传递函数exp(-D)将任意两个因子之间的距离转换为不同规则之间的相似度值，从而开发出不同的规则聚类方法。台湾电力公司确定的七个解释因素将影响台湾的用电量，并将这七个因素输入WEFuNN以预测未来的电力需求。这些历史数据将被用于训练WEFuNN，然后预测未来的电力需求。最后，将该模型与文献中提出的其他方法进行了比较。实验结果表明，WEFuNN模型的MAPE为6.11%，优于其他模型。综上所述，WEFuNN模型可作为台湾地区电力需求预测的实用工具。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

2009 First Asian Conference on Intelligent Information and Database Systems

自引率

0.00%

发文量