دانلود مقاله ISI انگلیسی شماره 107948
ترجمه فارسی عنوان مقاله

الگوهای تدریجی معدن در داده های عملیاتی بزرگ برای افزایش کارایی انرژی ساختمان

عنوان انگلیسی
Mining Gradual Patterns in Big Building Operational Data for Building Energy Efficiency Enhancement
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
107948 2017 6 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Energy Procedia, Volume 143, December 2017, Pages 119-124

ترجمه کلمات کلیدی
معدن گام به گام داده کاوی، دانش کشف، ایجاد عملکرد عملیاتی، بهره وری انرژی ساختمان،
کلمات کلیدی انگلیسی
Gradual pattern mining; Data mining; Knowledge discover; Building operational performance; Building energy efficiency;
پیش نمایش مقاله
پیش نمایش مقاله  الگوهای تدریجی معدن در داده های عملیاتی بزرگ برای افزایش کارایی انرژی ساختمان

چکیده انگلیسی

The advance in information technology has enabled the real-time monitoring and controls over building operations. Massive amounts of building operational data are being collected and available for knowledge discovery. Advanced data analytics are urgently needed to fully realize the potential of big building operational data in enhancing building energy efficiency. Data mining (DM) technology, which is renowned for its excellence in discovering hidden knowledge from massive datasets, has attracted increasing attention from the building industry. The rapid development in DM has provided powerful mining methods for extracting insights in various knowledge representations. Gradual pattern mining is a promising technique for identifying interesting patterns in big data. The knowledge discovered is represented as gradual rules, i.e., ‘the more/less A, the more/less B’. It can bring special interests to building energy management by highlighting co-variations among building variables. This paper investigates the usefulness of gradual pattern mining in analysing massive building operational data. Together with the use of decision trees, motif discovery and association rule mining, a comprehensive mining method is developed to ensure the quality and applicability of the knowledge discovered. The method is validated through a case study, using the real-world data retrieved from an educational building in Hong Kong. It shows that novel and valuable insights on building operation characteristics can be obtained, based on which fault detection and optimal control strategies can be developed to enhance building operational performance.