CoChat
Sign Up
Cited by 22Solar Radiation and PhotovoltaicsPhotovoltaic System Optimization TechniquesEnergy Load and Power Forecasting

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Evaluation of Missing Data Imputation Methods for an Enhanced Distributed PV Generation Prediction

Sundararajan, Aditya, Sarwat, Arif I.·2020·Advances in Intelligent Systems and Computing
DOI 10.1007/978-3-030-32520-6_43
Open this paper in CoChat — free account

Overview

Integrity signals

Registry

Crossref · 10.1007/978-3-030-32520-6_43

Cites

4 of 4 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Flagged first, then by citations

Show all 4 references

Research works printed but not linked: 48

  1. Sundararajan, A., Olowu, T.O., Wei, L., Rahman, S., Sarwat, A.I.: A case study on the effects of partial solar eclipse on distributed photovoltaic systems and management areas. IET Smart Grid (2019) · DOI
  2. Sarwat, A.I., Amini, M., Domijan, A., Damjanovic, A., Kaleem, F.: Weather-based interruption prediction in the smart grid utilizing chronological data. J. Mod. Power Syst. Clean Energy 2, 308–315 (2015)
  3. Sundararajan, A., Khan, T., Moghadasi, A., Sarwat, A.I.: A survey on synchrophasor data quality and cybersecurity challenges, and evaluation of their interdependencies. J. Mod. Power Sys. Clean Energy, 1–19 (2018) · DOI
  4. Jeng, R.S., Kuo, C.Y., Ho, Y.H., Lee, M.F., Tseng, L.W., Fu, C.L., et al.: Missing data handling for meter data management system. In: Proceedings of the Fourth International Conference on Future Energy Systems. e-Energy 2013, pp. 275–276. ACM, New York (2013). https://doi.org/10.1145/2487166.2487204 · DOI
  5. Peppanen, J., Zhang, X., Grijalva, S., Reno, M.J.: Handling bad or missing smart meter data through advanced data imputation. In: 2016 IEEE Power Energy Society Innovative Smart Grid Technologies Conference (ISGT), pp. 1–5 (2016) · DOI
  6. Kodaira, D., Han, S.: Topology-based estimation of missing smart meter readings. MDPI Energies 11(224), 1–18 (2018)
  7. Majidpour, M., Chu, P., Gadh, R., Pota, H.R.: Incomplete data in smart grid: treatment of missing values in electric vehicle charging data. In: 2014 International Conference on Connected Vehicles and Expo (ICCVE), pp. 1041–1042 (2014) · DOI
  8. Genes, C., Esnaola, I., Perlaza, S.M., Ochoa, L.F., Coca, D.: Robust recovery of missing data in electricity distribution systems. IEEE Trans. Smart Grid, 1 (2018) · DOI
  9. Olowu, T.O., Jafari, M., Sarwat, A.I.: A multi-objective optimization technique for Volt-Var control with high PV penetration using genetic algorithm. In: 2018 North American Power Symposium (NAPS), pp. 1–6 (2018) · DOI
  10. Jurado, S., Nebot, A., Mugica, F., Mihaylov, M.: FIR forecasting strategies able to cope with missing data: a smart grid application. Appl. Soft Comput. 51, 225–238 (2017) · DOI
  11. Khalid, A., Sundararajan, A., Sarwat, A.I.: A multi-step predictive model to estimate Li-Ion state of charge for higher C-rates. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe) (2019) · DOI
  12. Olowu, T.O., Sundararajan, A., Moghaddami, M., Sarwat, A.: Fleet aggregation of photovoltaic systems: a survey and case study. In: 2019 IEEE Power Energy Society Innovative Smart Grid Technologies Conference (ISGT) (2019) · DOI
  13. Parvez, I., Sarwat, A.I., Wei, L., Sundararajan, A.: Securing metering infrastructure of smart grid: a machine learning and localization based key management approach. Energies, 9(9) (2016). https://www.mdpi.com/1996-1073/9/9/691 · DOI
  14. Sundararajan, A., Sarwat, A.I., Pons, A.: A survey on modality characteristics, performance evaluation metrics, and security for traditional and wearable biometric systems. ACM Comput. Surv. 52(2), 1–35 (2019) · DOI
  15. Wei, L., Sundararajan, A., Sarwat, A.I., Biswas, S., Ibrahim, E.: A distributed intelligent framework for electricity theft detection using Benford’s law and stackelberg game. In: Resilience Week. pp. 5–11 (2017) · DOI · Paper page
  16. Zhang, Y., Huang, T., Bompard, E.F.: Big data analytics in smart grids: a review. Energy Inf. 1(1), 8 (2018). https://doi.org/10.1186/s42162-018-0007-5 · DOI
  17. Olowu, T.O., Sundararajan, A., Moghaddami, M., Sarwat, A.I.: Future challenges and mitigation methods for high photovoltaic penetration: a survey. Electr. Power Syst. Res. (2018) · DOI · Paper page
  18. Jerez, J.M., Molina, I., Garcia-Laencina, P.J., Alba, E., Ribelles, N., Martin, M., et al.: Missing data imputation using statistical and machine learning methods in a real breast cancer problem. Artif. Intell. Med. 50, 105–115 (2010) · DOI
  19. Schmitt, P., Mandel, J., Guedj, M.: A comparison of six methods for missing data imputation. J. Biometrics Biostatistics 6(1), 1–6 (2015)
  20. Livera, A., Phinikarides, A., Makrides, G., Georghiou, G.E.: Impact of missing data on the estimation of photovoltaic system degradation rate. In: 2017 IEEE 44th Photovoltaic Specialist Conference (PVSC), pp. 1954–1958 (2017) · DOI
  21. Miao, X., Gao, Y., Guo, S., Liu, W.: Incomplete data management: a survey. Front. Comput. Sci. 12(1), 4–25 (2018) · DOI
  22. Kang, H.: The prevention and handling of the missing data. Korean Soc. Anesthesiologists 64(5), 402–406 (2013) · DOI · Paper page
  23. Addo, E.D.: Performance comparison of imputation algorithms on missing at random data. Master’s Thesis submitted to East Tennessee State University, pp. 1–129 (2018). https://dc.etsu.edu/cgi/viewcontent.cgi?article=4839&context=etd
  24. Horton, N.J., Kleinman, K.P.: Much ado about nothing: a comparison of missing data methods and software to fit incomplete data regression models. Am. Stat. 1, 79–90 (2011)
  25. Sundararajan, A., Sarwat, A.I.: Roadmap to prepare distribution grid-tied photovoltaic site data for performance monitoring. In: International Conference on Big Data, IoT and Data Science (BID), pp. 101–115 (2017) · DOI
  26. Anzalchi, A., Sundararajan, A., Moghadasi, A., Sarwat A. power quality and voltage profile analyses of high penetration grid-tied photovoltaics: a case study. In: 2017 IEEE Industry Applications Society Annual Meeting, pp. 1–8 (2017) · DOI
  27. Zhang, Z.: Missing data exploration: highlighting graphical presentation of missing pattern. Ann. Transl. Med. 3(22), 356–362 (2015)
  28. Cheng, X., Cook, D., Hofmann, H.: Visually exploring missing values in multivariable data using a graphical user interface. J. Stat. Softw. 68(6), 1–23 (2015) · DOI
  29. Rubin, D.B.: Inference and missing data. Biometrika 63(3), 581–592 (1976) · DOI · Paper page
  30. Little, R.J.A., Rubin, D.B.: Statistical Analysis with Missing Data. 2nd edn. (2002). https://www.wiley.com/en-us/Statistical+Analysis+with+Missing+Data%2C+2nd+Edition-p-9780471183860 · DOI · Paper page
  31. Jamshidian, M., Jalal, S.: Tests of Homoscedasticity, Normality, and Missing Completely at Random for Incomplete Multivariate Data. J. Psychometrika 75(4), 649–674 (2010) · DOI
  32. Srivastava, M.S., Dolatabadi, M.: Multiple imputation and other resampling schemes for imputing missing observations. J. Multivar. Anal. 100(9), 1919–1937 (2009) · DOI
  33. Rayner, G.D., Rayner, J.C.W.: Power of the Neyman smooth tests for the uniform distribution. J. Appl. Math. Decis. Sci. 5(3), 181–191 (2001) · DOI
  34. Yu, B.P., Lemeshko, B.: A review of the properties of tests for uniformity. In: 12th International Conference on Actual Problems of Electronics Instrument Engineering (APEIE), vol. 1 (2014)
  35. Jamshidian, M., Jalal, S., Jansen, C.: MissMech: an R package for testing homoscedasticity, multivariate normality, and missing completely at random (MCAR). J. Stat. Softw. 56(6), 1–31 (2014) · DOI
  36. Gelman, A.: Missing-data imputation. In: Data Analysis Using Regression and Multilevel/Hierarchical Models, pp. 529–544 (2006) · DOI
  37. Zhang, Z.: Missing data imputation: focusing on single imputation. Ann. Transl. Med. 1, 1–8 (2016)
  38. Lodder, P.: To Impute or not Impute: That’s the Question. Paper Methodological Advice, University of Amsterdam, pp. 1–7. http://www.paultwin.com/wp-content/uploads/Lodder_1140873_Paper_Imputation.pdf
  39. Dempster, A.P., Laird, M.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Stat. Soc. B. 39(1), 1–38 (1977). www.jstor.org/stable/2984875 · DOI · Paper page
  40. Beretta, L., Santaniello. A.: Nearest neighbor imputation algorithms: a critical evaluation. In: 5th Translational Bioinformatics Conference (TBC), no. 3, pp. 198–208 (2015) · DOI
  41. Stekhoven, D.J., Buhlmann, P.: MissForest-non-parametric missing value imputation for mixed-type data. J. Bioinform. 1, 112–118 (2012) · DOI · Paper page
  42. Khalid, A., Sundararajan, A., Acharya, I., Sarwat, A.I.: Prediction of Li-Ion battery state of charge using multilayer perceptron and long short-term memory models. In: 2019 IEEE Transportation Electrification Conference (ITEC) (2019) · DOI
  43. Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: 13th International Conference on Artificial Intelligence and Statistics (AISTATS), pp. 1–8 (2010)
  44. Nguyen, D., Widrow, B.: Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights. In: 1990 IJCNN International Joint Conference on Neural Networks, vol. 3, pp. 21–26 (1990) · DOI
  45. Kalman, B.L., Kwasny, S.C.: Why tanh: choosing a sigmoidal function. In: Proceedings of IJCNN International Joint Conference on Neural Networks. vol. 4, pp. 578–581 (1992)
  46. Karlik, B., Olgac, A.V.: Performance analysis of various activation functions in generalized MLP architectures of neural networks. Int. J. Artif. Intell. Expert Syst. 4, 111–122 (2011)
  47. Zheng, H., Yang, Z., Liu, W., Liang, J., Li, Y.: Improving deep neural networks using softplus units. In: 2015 International Joint Conference on Neural Networks (IJCNN), pp. 1–4 (2015)
  48. Kingma, D.P., Ba, J.L.: Adam: a method for Stochastic Optimization. In: International Conference on Machine Learning, pp. 1–15 (2015)

Cited by

Sorted by citations

See all 22

Metadata

Authors
Sundararajan, A., & Sarwat, A. I.
Affiliations
Florida International University (US)
Corresponding author
Sarwat, Arif I.
ORCID
Venue
Advances in Intelligent Systems and Computing
Publisher
Springer International Publishing
Published
2019-10-13
Pages
590-609
ISSN
  • 2194-5357 (ISSN-L)
  • 2194-5365
DOI
10.1007/978-3-030-32520-6_43
OpenAlex
W2979735237
Cited by
22
References
4
Citations by year
2025: 3 · 2024: 3 · 2023: 2 · 2022: 5 · 2021: 5 · 2020: 1 · 2019: 3
FWCI
1.34
Type
conference paper
Language
en
Open access
closed
Licence
www.springer.com/tdm (tdm)
Locations
Advances in intelligent systems and computing (publishedVersion)
Topics
  • Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Solar Radiation and Photovoltaics
  • Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Photovoltaic System Optimization Techniques
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Energy Load and Power Forecasting
Keywords
missing data imputation, distributed photovoltaic generation, missing data mechanisms, k-nearest neighbors imputation, multiple imputation, expectation-maximization algorithm, irradiance, module temperature, ambient temperature, multilayer perceptron, grid-connected photovoltaic systems, distribution planning

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review