An Approximation Computation Approach to Big Data Analysis with a Case Analysis of PV System
2023 (English)In: 8th International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2023, Institute of Electrical and Electronics Engineers Inc. , 2023, p. 44-52Conference paper, Published paper (Refereed)
Abstract [en]
In the era of big data, it is indispensable to apply data science and technology for big data analysis to solve the big data problems. With the advancement of the big data technologies, we are also facing many problems when dealing with the big data and their studies. It is obvious that big data become "bigger"and "bigger", more complex than before, with a good number of attributes and features in various formats and styles. On the other hand, many data analysis techniques have been proposed for various application domain problems in different purposes. This worsens the situation of choosing an appropriate method for a right problem of right data. In this paper, the author intends to propose an approximation approach toward this problem, through discussing the ways of identification of patterns of the original data, be they of data features or analysis methods. The author attempts to apply the idea to a case of fault detection of a household photovoltaic system. © 2023 IEEE.
Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2023. p. 44-52
Keywords [en]
approximation, big data, data analysis, fault detection, methodologies, photovoltaic systems, Data handling, Information analysis, Case analysis, Data analysis techniques, Data problems, Data technologies, Faults detection, Methodology, PV system, Science and Technology
National Category
Information Systems
Identifiers
URN: urn:nbn:se:du-46631DOI: 10.1109/ICCCBDA56900.2023.10154583ISI: 001021400000009Scopus ID: 2-s2.0-85164675555ISBN: 9781665455336 (print)OAI: oai:DiVA.org:du-46631DiVA, id: diva2:1785739
Conference
8th International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2023, Chengdu, 26-28 April 2023
2023-08-042023-08-042023-08-24Bibliographically approved