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Spatio-Temporal Denoising Graph Autoencoders with Data Augmentation for Photovoltaic Data Imputation

Authors:

Raymond Wieser,

Avishai Shaton,

Jean-Nicolas Jaubert,

Robert Flottemesch,

Michael Howell,

Jennifer Braid,

Laura Bruckman,

Yinghui WuAuthors Info & Claims

Proceedings of the ACM on Management of Data, Volume 1, Issue 1

Article No.: 50, Pages 1 - 19

https://doi.org/10.1145/3588730

Published: 30 May 2023 Publication History

Abstract

The integration of the global Photovoltaic (PV) market with real time data-loggers has enabled large scale PV data analytical pipelines for power forecasting and reliability assessment of PV fleets. Nevertheless, the performance of PV data analysis depends on the quality of PV timeseries data. We propose a novel Spatio-Temporal Denoising Graph Autoencoder STD-GAE framework to impute missing PV Power Data. STD-GAE exploits temporal correlation, spatial coherence, and value dependencies from domain knowledge to recover missing data. It is empowered by two modules. (1) To cope with sparse yet various scenarios of missing data, STD-GAE incorporates a domain-knowledge aware data augmentation module to create plausible variations of missing data patterns. This generalizes STD-GAE to robust imputation over different seasons and environment. (2) STD-GAE nontrivially integrates spatiotemporal graph convolution layers and denoising autoencoder to improve the accuracy of imputation accuracy at PV fleet level. Experimental results on two PV datasets show that STD-GAE can achieve a gain of 43.14% in imputation accuracy and remains less sensitive to missing rate, different seasons, and missing scenarios, compared with state-of-the-art data imputation methods.

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Cited By

Lee DSon S(2024)Weighted Average Ensemble-Based PV Forecasting in a Limited Environment with Missing Data of PV PowerSustainability10.3390/su1610406916:10(4069)Online publication date: 13-May-2024
https://doi.org/10.3390/su16104069
Yan MWang YPang KXie MLi JBaeza-Yates RBonchi F(2024)Efficient Mixture of Experts based on Large Language Models for Low-Resource Data PreprocessingProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671873(3690-3701)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671873
Obata KKawabata KMatsubara YSakurai YBaeza-Yates RBonchi F(2024)Mining of Switching Sparse Networks for Missing Value Imputation in Multivariate Time SeriesProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671760(2296-2306)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671760
Show More Cited By

Index Terms

Spatio-Temporal Denoising Graph Autoencoders with Data Augmentation for Photovoltaic Data Imputation
1. Information systems
  1. Information systems applications
    1. Spatial-temporal systems
      1. Sensor networks

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cover image Proceedings of the ACM on Management of Data

Proceedings of the ACM on Management of Data Volume 1, Issue 1

PACMMOD

May 2023

2807 pages

EISSN:2836-6573

DOI:10.1145/3603164

Editor:
Divyakant Agrawal
UC Santa Barbara, United States

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Copyright © 2023 ACM.

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Publication History

Published: 30 May 2023

Published in PACMMOD Volume 1, Issue 1

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Cited By

Lee DSon S(2024)Weighted Average Ensemble-Based PV Forecasting in a Limited Environment with Missing Data of PV PowerSustainability10.3390/su1610406916:10(4069)Online publication date: 13-May-2024
https://doi.org/10.3390/su16104069
Yan MWang YPang KXie MLi JBaeza-Yates RBonchi F(2024)Efficient Mixture of Experts based on Large Language Models for Low-Resource Data PreprocessingProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671873(3690-3701)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671873
Obata KKawabata KMatsubara YSakurai YBaeza-Yates RBonchi F(2024)Mining of Switching Sparse Networks for Missing Value Imputation in Multivariate Time SeriesProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671760(2296-2306)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671760
Fan YWieser RBruckman LFrench RWu YSerra ESpezzano F(2024)Parallel-friendly Spatio-Temporal Graph Learning for Photovoltaic Degradation Analysis at ScaleProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680026(4470-4478)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3680026
Bian LYang WXu JTan ZSerra ESpezzano F(2024)Discovering Denial Constraints Based on Deep Reinforcement LearningProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679714(120-129)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679714
Wieser RFan YYu XBraid JShaton AHoffman ASpurgeon BGibbons DBruckman LWu YFrench R(2023)Time-Series Imputation Using Graph Neural Networks and Denoising Autoencoders2023 IEEE 50th Photovoltaic Specialists Conference (PVSC)10.1109/PVSC48320.2023.10359805(1-4)Online publication date: 11-Jun-2023
https://doi.org/10.1109/PVSC48320.2023.10359805
Nihar ACiardi TChawla RAkanbi OChaudhary VWu YFrench R(2023)Accelerating Time to Science using CRADLE: A Framework for Materials Data Science2023 IEEE 30th International Conference on High Performance Computing, Data, and Analytics (HiPC)10.1109/HiPC58850.2023.00041(234-245)Online publication date: 18-Dec-2023
https://doi.org/10.1109/HiPC58850.2023.00041

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