NDLI: Forecast uncertainty-based performance degradation diagnosis of solar PV systems

Content Provider	IET Digital Library
Author	Lee, Hyun Yong Ko, Seok Kap Lee, Byung Tak
Abstract	In this study, the authors are interested in estimating how much a PV system underperforms than expected by exploiting forecast uncertainty. For this, they first study a forecast accuracy-related forecast uncertainty metric using the ensemble method based on the dropout technique, which is widely used in deep learning forecasting models. Given the forecast accuracy-related uncertainty metric, the rationale of the authors' approach is that forecast accuracy is likely to decrease compared to the normal case of similar uncertainty metric values if any performance degradation happens. It is because similar uncertainty metric values are likely to show similar forecast accuracy. Therefore, they generate a standard table by simulating possible performance degradation cases and conduct the performance degradation diagnosis by looking up the standard table based on the uncertainty metric. From the experiments, in the case of persistent degradation, they show that their approach estimates the performance degradation with the estimation error of around 1% while an uncertainty-unaware approach shows the estimation error of up to 5%. In the case of temporal degradation, their approach shows the estimation error of around 3%, while the uncertainty-unaware approach does not show meaningful result.
Starting Page	968
Ending Page	975
Page Count	8
ISSN	17521416
Volume Number	14
e-ISSN	17521424
Issue Number	Issue 6, Apr (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-rpg/14/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-rpg.2019.1121
Journal	IET Renewable Power Generation
Publisher Date	2020-01-03
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Accuracy-related Uncertainty Metric Deep Learning Forecasting Model Estimation Error Forecast Accuracy-related Forecast Uncertainty Metric Forecast Uncertainty-based Performance Degradation Diagnosis Knowledge Engineering Technique Learning in AI Load Forecasting Performance Degradation Cases Persistent Degradation Photovoltaic Power System Power Engineering Computing Power System Planning And Layout PV System Solar Photovoltaic System Solar Power Stations Solar PV System Standard Table Temporal Degradation Uncertainty Metric Value Uncertainty-unaware Approach
Content Type	Text
Resource Type	Article
Subject	Renewable Energy, Sustainability and the Environment

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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