Change‐Point Detection on Solar Panel Performance Using Thresholded LASSO
dc.contributor.author | Choe, Youngjun | |
dc.contributor.author | Guo, Weihong | |
dc.contributor.author | Byon, Eunshin | |
dc.contributor.author | Jin, Jionghua (Judy) | |
dc.contributor.author | Li, Jingjing | |
dc.date.accessioned | 2017-01-06T20:49:37Z | |
dc.date.available | 2018-01-08T19:47:52Z | en |
dc.date.issued | 2016-12 | |
dc.identifier.citation | Choe, Youngjun; Guo, Weihong; Byon, Eunshin; Jin, Jionghua (Judy); Li, Jingjing (2016). "Change‐Point Detection on Solar Panel Performance Using Thresholded LASSO." Quality and Reliability Engineering International 32(8): 2653-2665. | |
dc.identifier.issn | 0748-8017 | |
dc.identifier.issn | 1099-1638 | |
dc.identifier.uri | https://hdl.handle.net/2027.42/135028 | |
dc.publisher | John Wiley & Sons, Ltd | |
dc.subject.other | quality control | |
dc.subject.other | solar energy | |
dc.subject.other | time series | |
dc.subject.other | reliability | |
dc.title | Change‐Point Detection on Solar Panel Performance Using Thresholded LASSO | |
dc.type | Article | en_US |
dc.rights.robots | IndexNoFollow | |
dc.subject.hlbsecondlevel | Management | |
dc.subject.hlbsecondlevel | Mathematics | |
dc.subject.hlbtoplevel | Business and Economics | |
dc.subject.hlbtoplevel | Science | |
dc.description.peerreviewed | Peer Reviewed | |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/135028/1/qre2077.pdf | |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/135028/2/qre2077_am.pdf | |
dc.identifier.doi | 10.1002/qre.2077 | |
dc.identifier.source | Quality and Reliability Engineering International | |
dc.identifier.citedreference | Meinshausen N, Yu B. Lasso‐type recovery of sparse representations for high‐dimensional data. The Annals of Statistics 2009; 37 ( 1 ): 246 – 270. | |
dc.identifier.citedreference | Lombardo T. What is the lifespan of a solar panel?, April 2014. Available from: http://www.engineering.com/ElectronicsDesign/ElectronicsDesignArticles/ArticleID/7475/What-Is-the-Lifespan-of-a-Solar-Panel.aspx. [Accessed on 15 April 2016]. | |
dc.identifier.citedreference | Jordan DC, Kurtz SR. Photovoltaic degradation rates – an analytical review. Progress in Photovoltaics: Research and Applications 2013; 21 ( 1 ): 12 – 29. | |
dc.identifier.citedreference | WeatherSpark.com. Historical weather for 2004 in Honolulu, Hawaii, USA. Available from: https://weatherspark.com/ history/33125/2004/Honolulu-Hawaii-United-States. [Accessed on 15 April 2016]. | |
dc.identifier.citedreference | U.S. Climate Data. Hawaii. Available from: http://www.usclimatedata.com/climate/hawaii/united-states/3181. [Accessed on 15 April 2016]. | |
dc.identifier.citedreference | Roy S, Atchade Y, Michailidis G. Change‐point estimation in high‐dimensional Markov random field models. arXiv preprint arXiv:1405.6176 2014. | |
dc.identifier.citedreference | Bai J. Estimation of a change point in multiple regression models. Review of Economics and Statistics 1997; 79 ( 4 ): 551 – 563. | |
dc.identifier.citedreference | Harchaoui Z, Lévy‐Leduc C. Multiple change‐point estimation with a total variation penalty. Journal of the American Statistical Association 2010; 105 ( 492 ): 1480 – 1493. | |
dc.identifier.citedreference | Killick R, Fearnhead P, Eckley IA. Optimal detection of changepoints with a linear computational cost. Journal of the American Statistical Association 2012; 107 ( 500 ): 1590 – 1598. | |
dc.identifier.citedreference | Brodsky BE, Darkhovsky BS. Nonparametric Methods in Change Point Problems. Springer: Dordrecht, 1993. | |
dc.identifier.citedreference | Carlstein EG, Müller HG, Siegmund D. Change‐point Problems. IMS Monograph. IMS: Hayward, CA, 1994. | |
dc.identifier.citedreference | Fryzlewicz P. Wild binary segmentation for multiple change‐point detection. The Annals of Statistics 2014; 42 ( 6 ): 2243 – 2281. | |
dc.identifier.citedreference | Harchaoui Z, Lévy‐Leduc C. Catching change‐points with lasso, in Advances in Neural Information Processing Systems MIT Press: Cambridge, MA, 2008; 617 – 624. | |
dc.identifier.citedreference | Zhou S. Thresholded lasso for high dimensional variable selection and statistical estimation. arXiv preprint arXiv:1002.1583 2010. | |
dc.identifier.citedreference | Choe Y, Guo W, Byon E, Jin J, Li J. Change‐point detection in solar panel performance analysis. In Proceedings of the 2016 Industrial and Systems Engineering Research Conference: Anaheim, CA, 2016. | |
dc.identifier.citedreference | Efron B, Hastie T, Johnstone I, Tibshirani R. Least angle regression. The Annals of Statistics 2004; 32 ( 2 ): 407 – 499. | |
dc.identifier.citedreference | Zhao P, Yu B. On model selection consistency of lasso. Journal of Machine Learning Research 2006; 7: 2541 – 2563. | |
dc.identifier.citedreference | van de Geer S, Bühlmann P, Zhou S. The adaptive and the thresholded lasso for potentially misspecified models (and a lower bound for the lasso). Electronic Journal of Statistics 2011; 5: 688 – 749. | |
dc.identifier.citedreference | Keener RW. Theoretical Statistics: Topics for a Core Course. Springer‐Verlag: New York, 2010. | |
dc.identifier.citedreference | Zou H. The adaptive lasso and its oracle properties. Journal of the American Statistical Association 2006; 101 ( 476 ): 1418 – 1429. | |
dc.identifier.citedreference | Boysen L, Kempe A, Liebscher V, Munk A, Wittich O. Consistencies and rates of convergence of jump‐penalized least squares estimators. The Annals of Statistics 2009; 37 ( 1 ): 157 – 183. | |
dc.identifier.citedreference | Donoho DL, Johnstone IM. Adapting to unknown smoothness via wavelet shrinkage. Journal of the American Statistical Association 1995; 90 ( 432 ): 1200 – 1224. | |
dc.identifier.citedreference | Kendall MG, Stuart A. The Advanced Theory of Statistics, Vol. 3. Macmillan: London, 1983. | |
dc.identifier.citedreference | U.S. Energy Information Administration. Annual energy outlook 2015 with projections to 2040, Technical Report DOE/EIA‐0383, U.S. Department of Energy,Washington, DC, 2015. | |
dc.identifier.citedreference | Green Tech Media Research and Solar Energy Industries Association (SEIA). U.S. solar market insight: Year‐in‐review 2012, Technical Report San Francisco, CA, 2013. | |
dc.identifier.citedreference | Green Tech Media Research. Global PV demand outlook 2015–2020: Exploring risk in downstream solar markets, Technical Report Boston, MA, 2015. | |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
Files in this item
Remediation of Harmful Language
The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.
Accessibility
If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.