Analyzing big time series data in solar engineering using features and PCA

Yang, D., Dong, Z., Lim, L. H. I. and Liu, L. (2017) Analyzing big time series data in solar engineering using features and PCA. Solar Energy, 153, pp. 317-328. (doi: 10.1016/j.solener.2017.05.072)

Abstract

In solar engineering, we encounter big time series data such as the satellite-derived irradiance data and string-level measurements from a utility-scale photovoltaic (PV) system. While storing and hosting big data are certainly possible using today’s data storage technology, it is challenging to effectively and efficiently visualize and analyze the data. We consider a data analytics algorithm to mitigate some of these challenges in this work. The algorithm computes a set of generic and/or application-specific features to characterize the time series, and subsequently uses principal component analysis to project these features onto a two-dimensional space. As each time series can be represented by features, it can be treated as a single data point in the feature space, allowing many operations to become more amenable. Three applications are discussed within the overall framework, namely (1) the PV system type identification, (2) monitoring network design, and (3) anomalous string detection. The proposed framework can be easily translated to many other solar engineer applications.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Lim, Dr Li Hong Idris
• Authors: Yang, D., Dong, Z., Lim, L. H. I., and Liu, L.
• College/School: College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
• Journal Name: Solar Energy
• Publisher: Elsevier
• ISSN: 0038-092X
• ISSN (Online): 1471-1257
• Published Online: 01 June 2017
• Copyright Holders: Copyright © 2017 Elsevier Ltd.
• First Published: First published in Solar Energy 153:317-328
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher