电力消耗预测数据挖掘的预测建模方法

N. Kaur, A. Kaur
{"title":"电力消耗预测数据挖掘的预测建模方法","authors":"N. Kaur, A. Kaur","doi":"10.1109/CONFLUENCE.2016.7508138","DOIUrl":null,"url":null,"abstract":"This paper presents an approach of data mining technique to predict electricity demand of a geographical region based on the meteorological conditions. The value prediction predictive data mining technique is implemented with the Artificial Neural Networks. The values of the factors such as temperature, humidity and public holiday on which electricity consumption depends and the daily consumption values constitute the data. Data mining operations are performed on this historical data to form a prediction model which is capable of predicting daily consumption provided the meteorological parameters. The steps of knowledge discovery of data process are implemented. The data is preprocessed and fed to neural network for training it. The trained neural network is used to predict the electricity demand for the given meteorological conditions.","PeriodicalId":299044,"journal":{"name":"2016 6th International Conference - Cloud System and Big Data Engineering (Confluence)","volume":"119 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"16","resultStr":"{\"title\":\"Predictive modelling approach to data mining for forecasting electricity consumption\",\"authors\":\"N. Kaur, A. Kaur\",\"doi\":\"10.1109/CONFLUENCE.2016.7508138\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents an approach of data mining technique to predict electricity demand of a geographical region based on the meteorological conditions. The value prediction predictive data mining technique is implemented with the Artificial Neural Networks. The values of the factors such as temperature, humidity and public holiday on which electricity consumption depends and the daily consumption values constitute the data. Data mining operations are performed on this historical data to form a prediction model which is capable of predicting daily consumption provided the meteorological parameters. The steps of knowledge discovery of data process are implemented. The data is preprocessed and fed to neural network for training it. The trained neural network is used to predict the electricity demand for the given meteorological conditions.\",\"PeriodicalId\":299044,\"journal\":{\"name\":\"2016 6th International Conference - Cloud System and Big Data Engineering (Confluence)\",\"volume\":\"119 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"16\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 6th International Conference - Cloud System and Big Data Engineering (Confluence)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CONFLUENCE.2016.7508138\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 6th International Conference - Cloud System and Big Data Engineering (Confluence)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CONFLUENCE.2016.7508138","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 16

摘要

提出了一种基于气象条件的数据挖掘技术预测地理区域电力需求的方法。利用人工神经网络实现了数值预测预测数据挖掘技术。用电量所依赖的温度、湿度、公众假期等因素的数值与每日用电量的数值构成数据。对这些历史数据进行数据挖掘,形成一个预测模型,在提供气象参数的情况下,可以预测出每天的用电量。实现了数据处理的知识发现步骤。数据经过预处理后输入神经网络进行训练。利用训练好的神经网络对给定气象条件下的电力需求进行预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predictive modelling approach to data mining for forecasting electricity consumption
This paper presents an approach of data mining technique to predict electricity demand of a geographical region based on the meteorological conditions. The value prediction predictive data mining technique is implemented with the Artificial Neural Networks. The values of the factors such as temperature, humidity and public holiday on which electricity consumption depends and the daily consumption values constitute the data. Data mining operations are performed on this historical data to form a prediction model which is capable of predicting daily consumption provided the meteorological parameters. The steps of knowledge discovery of data process are implemented. The data is preprocessed and fed to neural network for training it. The trained neural network is used to predict the electricity demand for the given meteorological conditions.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信