An evermore accelerated deployment of photovoltaic (PV) capacity is expected worldwide and in-situ solar irradiance time series play a decisive in supporting such growth: not only they represent the foundation of solar resource assessment and forecasting, but they also drive prospective PV yield studies, can be used as a calibration reference when using satellite data, evaluating PV systems’ performance, or even developing forecasting algorithms.
However, such datasets inevitably have gaps (i.e., periods with missing data) – as a result of defaults during data-logging, sensor failures, among others, or from Quality Check (QC) procedures – that can compromise their applicability and value. An additional issue is that data gaps can be further enlarged when computing temporal aggregations, notably for intra-daily to daily, daily to monthly and yearly averages, thus further degrading the dataset.
This has raised the need for gap-filling (GF) methods that can post‑process either static historical datasets or more dynamic real-time data streams. Each case is characterised by different constraints, such as the access to data that follows a given data gap or the acceptable time lag for generating the replacement synthetic data.
And while, naturally, a given gap-filling method can be tested for a given location, such analyses are done in a very context-specific manner. Thus, this report aims to propose a GF benchmark framework, as well as evaluate a set of possible baseline algorithms for:
It is also important to mention that while the focus is exclusively on:
This report is the result of expert discussions during specific sessions of the subtask-2 activity 2.1 of the Task 16 of PVPS and during the dedicated workshop organised during the ICEM 2019 conference: “Workshop on best practices for automatic and expert-based data quality control methods and for gap filling methods”.
The main authors have identified the following 3 Key Takeaways from the report: