构建混合智能预测系统的工具集:在需水量预测中的应用

Narate Lertpalangsunti , Christine W. Chan , Ralph Mason , Paitoon Tontiwachwuthikul
{"title":"构建混合智能预测系统的工具集:在需水量预测中的应用","authors":"Narate Lertpalangsunti ,&nbsp;Christine W. Chan ,&nbsp;Ralph Mason ,&nbsp;Paitoon Tontiwachwuthikul","doi":"10.1016/S0954-1810(98)00008-9","DOIUrl":null,"url":null,"abstract":"<div><p>This paper presents the Intelligent Forecasters Construction Set (IFCS) which is a toolset for constructing forecasting applications. The toolset supports the intelligent techniques of fuzzy logic, artificial neural networks, knowledge-based and case-based reasoning. The developer can construct a forecasting application using rules, procedures and flow diagrams, which are organized into a hierarchy of workspaces. The modularity of the IFCS allows subsequent addition of other modules of intelligent techniques.</p><p>The IFCS was used for developing a water demand forecasting system based on real-world data obtained from the City of Regina's water distribution system and Environment Canada. A utility demand prediction system developed with the IFCS system is useful for optimizing operation costs of water plants. Some water plants need to pay a flat rate for electricity, which is set depending on peak kilowatt demand. Hence, if the peak kilowatt demand can be reduced, the operating costs of the plant can be lessened (Jamieson RA et al. American Water Works Association Journal 1993;85:48–55). An energy management system needs a good estimate of future customer demand in order to find the optimal pumping schedules that can minimize the peak kilowatt demand. Since the IFCS supports developing multiple predictor models, modeling of data can be expedited. The benefits of using multiple modules of artificial neural networks for demand prediction are presented. The results from this approach are compared with a linear regression and a case-based reasoning program. The performance comparisons among the forecasters will be discussed.</p></div>","PeriodicalId":100123,"journal":{"name":"Artificial Intelligence in Engineering","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"1999-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/S0954-1810(98)00008-9","citationCount":"49","resultStr":"{\"title\":\"A toolset for construction of hybrid intelligent forecasting systems: application for water demand prediction\",\"authors\":\"Narate Lertpalangsunti ,&nbsp;Christine W. Chan ,&nbsp;Ralph Mason ,&nbsp;Paitoon Tontiwachwuthikul\",\"doi\":\"10.1016/S0954-1810(98)00008-9\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>This paper presents the Intelligent Forecasters Construction Set (IFCS) which is a toolset for constructing forecasting applications. The toolset supports the intelligent techniques of fuzzy logic, artificial neural networks, knowledge-based and case-based reasoning. The developer can construct a forecasting application using rules, procedures and flow diagrams, which are organized into a hierarchy of workspaces. The modularity of the IFCS allows subsequent addition of other modules of intelligent techniques.</p><p>The IFCS was used for developing a water demand forecasting system based on real-world data obtained from the City of Regina's water distribution system and Environment Canada. A utility demand prediction system developed with the IFCS system is useful for optimizing operation costs of water plants. Some water plants need to pay a flat rate for electricity, which is set depending on peak kilowatt demand. Hence, if the peak kilowatt demand can be reduced, the operating costs of the plant can be lessened (Jamieson RA et al. American Water Works Association Journal 1993;85:48–55). An energy management system needs a good estimate of future customer demand in order to find the optimal pumping schedules that can minimize the peak kilowatt demand. Since the IFCS supports developing multiple predictor models, modeling of data can be expedited. The benefits of using multiple modules of artificial neural networks for demand prediction are presented. The results from this approach are compared with a linear regression and a case-based reasoning program. The performance comparisons among the forecasters will be discussed.</p></div>\",\"PeriodicalId\":100123,\"journal\":{\"name\":\"Artificial Intelligence in Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1999-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1016/S0954-1810(98)00008-9\",\"citationCount\":\"49\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Artificial Intelligence in Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0954181098000089\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Artificial Intelligence in Engineering","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0954181098000089","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 49

摘要

本文提出了智能预测构建集(IFCS),这是一个构建预测应用程序的工具集。该工具集支持模糊逻辑、人工神经网络、基于知识的推理和基于案例的推理等智能技术。开发人员可以使用规则、过程和流程图来构建预测应用程序,它们被组织到工作空间的层次结构中。IFCS的模块化允许随后添加智能技术的其他模块。IFCS用于根据从里贾纳市供水系统和加拿大环境部获得的实际数据开发用水需求预测系统。利用IFCS系统开发的公用事业需求预测系统可用于优化水厂的运行成本。一些水厂需要支付统一的电费,这取决于峰值千瓦需求。因此,如果峰值千瓦需求可以降低,则工厂的运行成本可以降低(Jamieson RA等人)。美国水工程协会杂志1993;85:48-55)。能源管理系统需要很好地估计未来的客户需求,以便找到可以最小化峰值千瓦需求的最佳抽水计划。由于IFCS支持开发多个预测模型,因此可以加快数据建模。介绍了采用多模块人工神经网络进行需求预测的优点。该方法的结果与线性回归和基于案例的推理程序进行了比较。将讨论预测者之间的业绩比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A toolset for construction of hybrid intelligent forecasting systems: application for water demand prediction

This paper presents the Intelligent Forecasters Construction Set (IFCS) which is a toolset for constructing forecasting applications. The toolset supports the intelligent techniques of fuzzy logic, artificial neural networks, knowledge-based and case-based reasoning. The developer can construct a forecasting application using rules, procedures and flow diagrams, which are organized into a hierarchy of workspaces. The modularity of the IFCS allows subsequent addition of other modules of intelligent techniques.

The IFCS was used for developing a water demand forecasting system based on real-world data obtained from the City of Regina's water distribution system and Environment Canada. A utility demand prediction system developed with the IFCS system is useful for optimizing operation costs of water plants. Some water plants need to pay a flat rate for electricity, which is set depending on peak kilowatt demand. Hence, if the peak kilowatt demand can be reduced, the operating costs of the plant can be lessened (Jamieson RA et al. American Water Works Association Journal 1993;85:48–55). An energy management system needs a good estimate of future customer demand in order to find the optimal pumping schedules that can minimize the peak kilowatt demand. Since the IFCS supports developing multiple predictor models, modeling of data can be expedited. The benefits of using multiple modules of artificial neural networks for demand prediction are presented. The results from this approach are compared with a linear regression and a case-based reasoning program. The performance comparisons among the forecasters will be discussed.

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