From experimental to official statistics: The case of solar energy

Broe, Sofie De; Meijers, R.; Bosch, Olav ten; Buelens, Bart; Priem, Alex; Laevens, Ben; Jong, T. de; Puts, Marco

doi:10.3233/sji-180458

Cited by 6 publications

(1 citation statement)

References 8 publications

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“…Finally, a project is under way at SN to use machine learning techniques to identify missing solar panels. For more information on this topic, we refer the reader to section 3.2.1 in De Broe et al (2019).…”

Section: Solar Panel Databasementioning

confidence: 99%

Observational daily and regional photovoltaic solar energy production for the Netherlands

Laevens,

Bosch,

Pijpers

et al. 2020

Preprint

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This paper presents a new method for deriving the energy yield generated by photovoltaic solar energy systems in the Netherlands, on a daily and regional basis, in 2016 and 2017. We identify two new data sources to construct our methodology: pvoutput, an online portal with real-time solar energy yield information, and high resolution irradiance data, from the Royal Netherlands Meteorological Institute. Combining these sources, allows us to link irradiance and energy on a daily basis. We apply this information to our PV systems database, allowing us to derive daily and annual solar energy yields. We examine the variation in our daily and annual estimates as a result of taking different subsets of pvoutput systems with certain specifications such as orientation, tilt and inverter to PV capacity ratio. We obtain specific annual energy yields of 877-946 kW h/kW p and 838-899 kW h/kW p for 2016 and 2017 respectively, implying that the current method used at Statistics Netherlands underestimated and overestimated the 2016 and 2017 annual yields respectively. Finally, we translate our national estimates into municipality solar energy yields. This research demonstrates that an irradiance based measure of solar energy generation is necessary to produce more accurate energy yields on both a national and regional level.

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Section: Solar Panel Databasementioning

confidence: 99%