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Analysis and Impact Evaluation of Missing Data Imputation in Day-ahead PV Generation Forecasting

1
School of Integrated Technology, Gwangju Institute of Science and Technology, Gwangju 61005, Korea
2
Research Institute for Solar and Sustainable Energies, Gwangju Institute of Science and Technology, Gwangju 61005, Korea
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(1), 204; https://doi.org/10.3390/app9010204
Received: 6 December 2018 / Revised: 30 December 2018 / Accepted: 31 December 2018 / Published: 8 January 2019
(This article belongs to the Special Issue Applications of Artificial Neural Networks for Energy Systems)
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Abstract

Over the past decade, PV power plants have increasingly contributed to power generation. However, PV power generation widely varies due to environmental factors; thus, the accurate forecasting of PV generation becomes essential. Meanwhile, weather data for environmental factors include many missing values; for example, when we estimated the missing values in the precipitation data of the Korea Meteorological Agency, they amounted to ~16% from 2015–2016, and further, 19% of the weather data were missing for 2017. Such missing values deteriorate the PV power generation prediction performance, and they need to be eliminated by filling in other values. Here, we explore the impact of missing data imputation methods that can be used to replace these missing values. We apply four missing data imputation methods to the training data and test data of the prediction model based on support vector regression. When the k-nearest neighbors method is applied to the test data, the prediction performance yields results closest to those for the original data with no missing values, and the prediction model’s performance is stable even when the missing data rate increases. Therefore, we conclude that the most appropriate missing data imputation for application to PV forecasting is the KNN method. View Full-Text
Keywords: PV forecasting; missing data imputation; support vector regression; linear interpolation; mode imputation; k-nearest neighbors; multivariate imputation by chained equations PV forecasting; missing data imputation; support vector regression; linear interpolation; mode imputation; k-nearest neighbors; multivariate imputation by chained equations
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Kim, T.; Ko, W.; Kim, J. Analysis and Impact Evaluation of Missing Data Imputation in Day-ahead PV Generation Forecasting. Appl. Sci. 2019, 9, 204.

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